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Record ID: 60 [ Page 2 of 2, No. 1 ]
Biosurveillance of measles using control charts: A case study using NCR laboratory confirmed measles counts from January 2009 to January 2014
Authors: Lorraine Christelle B. Angkico; Priscilla A. Diaz; Robert Neil F. Leong; Frumencio F. Co
Abstract:
This paper aims to explore early outbreak detection methods for measles. Two methods adopted from statistical process control were modified and used to fit biosurveillance, namely Shewhart and Exponentially Weighted Moving Average (EWMA) charts. Seven variations of such control charts are proposed: two under Shewhart chart (normal-based and zero-inflated Poisson (ZIP)-based) and five under EWMA charts (?s of 0.05, 0.10, 0.15, 0.20, and 0.25). To study the proposed charts, daily counts of laboratory confirmed cases of measles in the National Capital Region from 2009 until 2014 were utilized to characterize both the disease background and outbreak equations. During this time span, three measles outbreaks have transpired. The proposed charts, set at average time between false signals (ATFSs) of both one and two months, were evaluated and compared using performance metrics such as conditional expected delay (CED), proportion of true signals (PTS), proportions of detections in an outbreak (PDO), and probability of successful detection (PSD), computed from 500 sets of simulated data. It was found that ZIP-based Shewhart and EWMA with a ? of 0.05 work best for ATFSs of one and two months, respectively. Health-governing bodies may seek to explore the possible utilization of these charts to improve measles surveillance.
Keywords: control charts, measles, early event detection, biosurveillance
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Year: 2014 Vol.: 63 No.: 2
Record ID: 59 [ Page 2 of 2, No. 2 ]
An efficient variant of dual to ratio and product estimator in sample surveys
Authors: Gajendra K. Vishwakarma; Raj K. Gangele; Ravendra Singh
Abstract:
In this paper, we propose a dual to ratio and product estimator for estimating finite population mean of study variable on applying simple transformation to auxiliary variable by using its average values in the population that are generally available in practice. The mean squared error of the proposed estimator have been obtained to the first degree of approximation. It has also been shown that the proposed estimator has greater applicability and is more efficient than the usual estimator even when, the existing estimators are less efficient. An empirical study is carried out to demonstrate the performance of proposed estimator.
Keywords: Auxiliary variable, Study variable, Mean square error, Population mean, Simple random sampling
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Year: 2014 Vol.: 63 No.: 2
Record ID: 58 [ Page 2 of 2, No. 3 ]
A general class of chain ratio-product type exponential estimators in double sampling using two auxiliary variates
Authors: Gajendra K. Vishwakarma; Manish Kumar; Raj K. Gangele
Abstract:
In this paper, a general class of chain ratio-product type exponential estimators has been proposed for estimating a finite population mean in presence of two auxiliary variates under double sampling scheme. The expressions for bias and mean square error (MSE) of the proposed class are derived up to the first degree of approximation. Also, the expression of asymptotic optimum estimator (AOE) in the proposed class is obtained. Some estimators are shown to be particular members of the proposed class. The proposed class has been compared for its precision with the usual unbiased estimator and several other estimators of the literature. In addition, an empirical study is also carried out in support of theoretical findings.
Keywords: Auxiliary variates, Study variate, Double Sampling, bias, mean square error.
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Year: 2014 Vol.: 63 No.: 2
Record ID: 57 [ Page 2 of 2, No. 4 ]
Modeling clustered survival data with cured fraction
Authors: Iris Ivy M. Gauran; Angela D. Nalica
Abstract:
In modelling lifetime data, standard parametric theory assumes that all observations will eventually experience the event of interest if they are monitored for a very long period. While every unit starts as susceptible to the event of interest, a fraction of observations may switch into a non-susceptible group. A mixture cured fraction model with covariates is modified to incorporate random clustering effect to characterize the switch mechanism. Simulation studies and telecommunications data show that cured fraction models with random clustering effect perform better than their parametric counterpart in terms of predictive ability. Moreover, results show that the nonparametric method is superior than modified parametric Cox PH model.
Keywords: Mixture Cured Fraction Models, Random Clustering Effect, Right-censored Lifetime Data
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Year: 2014 Vol.: 63 No.: 2
Record ID: 56 [ Page 2 of 2, No. 5 ]
Proceedings of the Focused Group Discussion on Accreditation/Certification for Professional Statisticians
Authors: PSAI Initiatives
Abstract:
FOREWORD
The Philippine Statistical Association, Inc. (PSAI) is a professional association dedicated to the promotion of Statistics as a science and a discipline. As such, it recognizes the need to pursue the development of the discipline and the continuing professional growth of its practitioners in the academe, the government and private sectors, and in the international community.
In 2008, the PSAI through the Institutional Development Committee (IDC) chaired by Mr. Tomas P. Africa, then Vice President and Chair of the IDC pursued the crafting and ratification of the Code of Ethics for Statisticians, and notes in the Foreword that:
"It has been an aspiration of the Philippine Statistical Association (PSA) to institute a system of accreditation or certification for Statistics professionals, similar to those existing in Australia, New Zealand, the United Kingdom and the United States. On at least two fronts, the label 'statistician' may have been misused and misappropriated by unscrupulous professionals.
The accreditation stage will deal with what would be the qualifications: education, work experience, research record as well as the behavior or ethical standards of the statistics practitioner. This Code addresses the latter. The necessary academic background, and work experience needed to bring about the conduct and/or behavior of such professionals may be deduced from this Code."
With the Code of Ethics for Statisticians firmly in place, the stage is set for the accreditation process. Under the same stewardship, Mr. Africa as Vice President and Chair of the Institutional Development Committee (2012-2013), concerned professionals were gathered to undertake the Focus Group Discussion (FGD), and to put into motion the work envisioned to initiate the development of a system for eventual accreditation and professional certification of practitioners in the statistics profession.
Keywords:
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Year: 2014 Vol.: 63 No.: 1
Record ID: 55 [ Page 2 of 2, No. 6 ]
Indentifying Influencers of Consumer Activity: A Case Study in Predictive Modeling
Authors: Angela D. Nalica; Joseph Ryan G. Lansangan
Abstract:
Marketing activation usually entails a universal blast of information to all consumers. Oftentimes, only a small proportion of the consumers react positively to such activation, resulting to waste in marketing expenses. If a circle of influencers can be identified for certain events or phenomena, then such activities can be focused into a group of factors or individuals, thus, optimizing the outcomes. With the identification of such group of influencers, resources for strategic optimization of outcomes can be allocated efficiently. A usage database is used to identify consumers who could initiate or influence the complex dynamics of consumer behavior. The data mining process of clustering, sampling, aggregation, modeling, and validation are used to mine such information from the database.
Keywords: logistic regression, segmentation, influencers, consumer behaviour, customer relationship management
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Year: 2014 Vol.: 63 No.: 1
Record ID: 54 [ Page 2 of 2, No. 7 ]
Effects of Household Use of Biomass Fuel and Kerosene on Birth Weight of Babies in the Philippines
Authors: Michael Daniel C. Lucagbo
Abstract:
Birth weight is an important indicator of a child’s health status. It is a significant factor of his or her risk of mortality and morbidity. Infants with low birth weight have been shown to be 40 times more likely to die within the first 28 days of birth than normal birth weight infants. Moreover, low birth weight infants exhibit a much higher incidence of neurological impairment, gross and fine motor dysfunction and developmental delay. Instead of going down to reduce the incidence of child mortality (which is one of the Millennium Development Goals), the incidence of low birth weight in the Philippines has gone the opposite direction: rising from 20.3% in 2003 to 21.2% in 2008. This paper tackles the very serious issue of birth weight using data from the 2008 National Demographic and Health Survey (NDHS), and focuses on one important risk factor: type of cooking fuel used in the household. Using the ordinal logistic regression model, the study establishes that the use of dirty cooking fuel (biomass fuel or kerosene) for daily use of cooking and heating is a significant environmental risk factor of low birth weight. Moreover, the results also show that maternal smoking is significantly associated with the size of the child at birth. Other demographic factors that may be associated with low birth weight are examined as well. Information about the effect cooking fuel on birth weight should lead the government and policymakers to make clean cooking fuel available to Philippine households at a cheap cost.
Keywords: Low birth weight, biomass fuel, maternal smoking, ordinal logistic regression
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Year: 2014 Vol.: 63 No.: 1
Record ID: 53 [ Page 2 of 2, No. 8 ]
Comparison of Different Methods of Constructing Housing Start Index in the Philippines
Authors: Felicidad Hebron
Abstract:
We investigate three methods of constructing housing start index with a fixed base year. In the Philippines, researchers and planners uses data on building permits to monitor construction sites where economic activities are expected to follow. Suppliers of construction materials such as cement, lumber, steel, among others, rely on these data for planning purposes. Other businesses like banks and food chains also use these data as proximate indicators of supply and demand for investment. A mixed model accounting the empirical relations between the index and other economic indicators they usually lead is used in the assessment of the index resulting from three different methods. There is a strong space-time association between the index and other indicators, confirming the relationship between the economic boom and housing start index. There is evidence that the index is capable of leading some key economic indicators.
Keywords: housing start index, leading indicators, mixed models
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Year: 2014 Vol.: 63 No.: 1
Record ID: 52 [ Page 2 of 2, No. 9 ]
Design Strategies in Fitting a Nonlinear Model
Authors: Michael Van Supranes
Abstract:
Estimation of parameters in a nonlinear model depends on the distribution of data points along various levels of curvature in the function to be estimated. Using Monte Carlo simulation, an optimal allocation procedure for building stratified designs was derived. The optimal allocation procedure conforms well to a proportionality property, directly relating the number of observations with the total curvature and measure or length of the domain. The proportionality property can be used to easily construct an allocation procedure that is near the optimal. Stratification results were applied and explored on uniform designs. Simulation results show that strategic stratification can improve the prediction accuracy of uniform designs.
Keywords: Stratification, Experimental Designs, Spline Regression, Monte Carlo Simulation
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Year: 2014 Vol.: 63 No.: 1
Record ID: 51 [ Page 2 of 2, No. 10 ]
Semiparametric Poisson Regression Model for Clustered Data
Authors: Eiffel A. de Vera
Abstract:
A semiparametric Poisson regression is proposed in modeling spatially clustered count data. The heterogeneous covariate effect across the clusters is formulated in the context of nonparametric regression while the random clustering effect is based on a parametric specification. We propose two estimation procedures: (1) the parametric and nonparametric parts are estimated simultaneously via penalized least squares; and (2) the parametric and nonparametric parts are estimated iteratively via the backfitting algorithm. The simulation study exhibited the advantages of these two methods over ordinary Poisson regression and an intrinsically linear model when the aggregate covariate effect is negligible. This happens when sensitivity to the covariate is minimal or the data-generating model is not linear. The two estimation methods are generally more advantageous over the traditional approaches when linear model fit is poor. In cases where the linear fit is good, the proposed methods are at par with the traditional methods, but the second approach can still be advantageous when there are several covariates involved since the backfitting algorithm yields computational simplicity in the estimation process.
Keywords: backfitting, generalized additive models, nonparametric regression, random effects
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Year: 2014 Vol.: 63 No.: 1
Record ID: 50 [ Page 2 of 2, No. 11 ]
Modelling Zero-Inflated Clustered Count Data: A Semiparametric Approach
Authors: Kevin Carl P. Santos
Abstract:
This paper proposes to use an additive semiparametric Poisson regression in modelling zero-inflated clustered data. Two estimation methods are exploited in this paper based on de Vera (2010). The first simultaneously estimates both the parametric and nonparametric parts of the model. The second utilizes the backfitting algorithm by smoothing the nonparametric function of the covariates and then estimating the parametric parts of the postulated model. The predictive accuracy, measured in terms of root mean square error (RMSE), of the proposed methods is compared to that of ordinary Zero-Inflated Poisson (ZIP) regression model. It is found out through simulation study that the average RMSE of the ordinary ZIP regression model is at most 81% and 27% higher for equal and unequal cluster sizes, respectively, than that of proposed model whose parametric and nonparametric parts are simultaneously estimated.
Keywords: Zero-Inflated Poisson models, clustered data, Generalized Additive Models, backfitting algorithm
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Year: 2014 Vol.: 63 No.: 1
Record ID: 49 [ Page 2 of 2, No. 12 ]
Autologistic Spatial-Temporal Modeling
Authors: Ma. Andriena Ida B. Del Ayre-Ofina
Abstract:
We postulate a combination of spatial-temporal and autologistic model in characterizing binary data collected over time and space. Using a second-order neighborhood system in defining the spatial component of the model, backfitting algorithm is used in estimating the model. As the incidence of success and failure responses becomes balanced, sensitivity and specificity increases. The predictive ability of the model is fairly robust to the spatial parameter but is significantly influenced by the temporal parameter. The bias of the estimate for the spatial parameter declines as it becomes dominant into the model. Furthermore, as the autocorrelation becomes stronger, its estimate becomes less biased. The backfitting algorithm is also observed to converge fast in the estimation of the spatial-temporal autologistic model.
Keywords: binary response, autologistic model, spatial-temporal model, backfitting
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Year: 2014 Vol.: 63 No.: 1
Record ID: 48 [ Page 2 of 2, No. 13 ]
Visual Exploration of Climate Variability
Authors: Wendell Q. Campano; Rona Mae U. Tadlas
Abstract:
In this paper, a data visualization framework for investigating and exploring climate time series data is introduced. This method utilizes the results obtained from performing series of cluster analysis based on a particular multivariate data set for each defined subset in the time series. The said approach is implemented to the climate data in the Philippines. The data image results obtained from the procedure revealed the expected overall climate pattern in the Philippines as well as some localized segments of climate changes in the time series which deviate from the overall pattern. A wavelet analysis which is a well established method in analyzing climate data is also done to validate the results shown by the proposed visualization method.
Keywords: information visualization; data image; cluster analysis; wavelet; climate change; climate variability; time series; multivariate data
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Year: 2013 Vol.: 62 No.: 2
Record ID: 47 [ Page 2 of 2, No. 14 ]
Measuring Income Mobility using Pseudo-Panel Data
Authors: Arturo M. Martinez Jr; Mark Western; Michele Haynes; Wojtek Tomaszewski
Abstract:
To reconcile the need of providing a more dynamic perspective of the evolution of income distribution with the lack of panel data, several techniques have been offered to construct pseudo-panel data from repeated cross-sectional surveys. Using actual panel data from the Philippines, this study evaluates the performance of four pseudo-panel techniques in measuring a wide array of income mobility indicators. Preliminary results suggest that methods with more flexible income model specifications perform better than those with highly parameterized models. More importantly, these flexible pseudo-panel procedures produced estimates of poverty dynamics and movement-based indices which are quite close to the estimates computed from the actual panel data. Nevertheless, further improvements are warranted to be able to develop a more satisfactory estimation procedure for indices measuring temporal dependence and the inequality-reducing effect of income mobility.
Keywords: panel survey; cross-sectional survey; temporal; dependence; income distribution
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Year: 2013 Vol.: 62 No.: 2
Record ID: 46 [ Page 2 of 2, No. 15 ]
Effects of Education on Climate Risk Vulnerability in the Philippines: Evidence from Regional Panel Data
Authors: Michael Daniel C. Lucagbo; Kristina Norma B. Cobrador; Nikki Ann M. de Mesa; Remy Faye M. Ferrera; Jennifer E. Marasigan
Abstract:
The effects of climate change are being felt disproportionately in the world’s poorest countries and among those groups of people least able to cope. The Philippines, being a storm-lashed nation, is one country having high climate change vulnerability and low climate change resilience. A number of researches have suggested investments on adaptation which place strong emphasis on reducing vulnerability to climate change. Focusing on climate change vulnerability in the Philippines, this study examines the effect of one particular type of government intervention: increasing the level of education. In this study, the effect of education on vulnerability to climate change is examined in a regional panel data analysis using official Philippine statistics from the Natural Disaster Risk Reduction and Management Council (NDRRMC), Labor Force Survey (LFS), National Statistical Coordination Board (NSCB). Using the fixed-effects Poisson (FEP) regression model, the study establishes that at the community level, the number of employed college graduates is a significant factor that reduces climate risk vulnerability (measured by a number of deaths from natural disasters), controlling for other factors such as number of disasters, gross regional domestic product (GRDP), and population size.
Keywords: Vulnerability, Resilience, Panel Data, Fixed-effects Poisson model
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Year: 2013 Vol.: 62 No.: 2
Record ID: 45 [ Page 2 of 2, No. 16 ]
Regression Analyses of the Philippine Birth Weight Distribution
Authors: Elline Jade Beltran; Robert Neil F. Leong; Frumencio F. Co
Abstract:
Low birth weight has both short-term and long-term effects. It can lead to complications among infants causing neonatal deaths. Several literatures also suggested relationships between low birth weight and delayed mental and physical development. These negative effects are further magnified in developing countries, one of which is the Philippines. In this paper, birth weight is analysed through logistic, ordinary least squares, and quantile regression techniques using a sample from the 2008 Philippine Birth Recode. Quantile regression results offer a more dynamic picture of how these correlates affect the conditional distribution of birth weight. The obtained estimates of the marginal effects of several demographical and maternal health correlates of birth weight suggest that socially and economically impoverished mothers are more likely to have low birth weight babies. These results would recommend a focus on improving maternal health care through proper education.
Keywords: birth weight; quantile regression; logistic regression; ordinary least squares
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Year: 2013 Vol.: 62 No.: 2
Record ID: 44 [ Page 2 of 2, No. 17 ]
Profitability and Growth Topology Analysis of Unilevel-type of Network Marketing Structures
Authors: John Carlo P. Daquis; Angelique O. Castaneda; Nelson D. Sy; Joseph V. Abgona
Abstract:
This study analyzes a type of multi-level marketing (MLM) structure through a simulation of MLM systems. In unilevel MLM, distributors earn from both sales from direct selling and commissions from recruitment of downlines. Several distributional assumptions were made in constructing the system, such as the use of the uniform, Bernoulli, and Poisson distributions. Member income is measured based on commission from recruit pay-ins in their downlines and income from direct selling. Based on the simulated unilevel MLM structures, the fundamental behavior of a unilevel MLM is captured and analyzed in terms of its network growth topology and profitability.
Keywords: multi-level marketing; network simulation; unilevel structure; complex systems; probability distributions
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Year: 2013 Vol.: 62 No.: 2
Record ID: 43 [ Page 2 of 2, No. 18 ]
Classification of Congenital Hypothyroidism using Artificial Neural Networks
Authors: Iris Ivy Gauran; Ma. Sofia Criselda A. Poblador
Abstract:
The Newborn Screening Reference Center (NSRC) of the National Health Institute in University of the Philippines Manila collects measurements from five attributes to determine whether Congenital Hypothyroidism (CH) is present in a neonate. Detecting the CH cases is a major concern of medical practitioners because it provides richer information than the healthy ones. However, because of the rarity of this metabolic condition, existing classification algorithms oftentimes misclassify a newborn as “normal” even if it is not. This paper investigates the efficiency of Self-Organizing Kohonen Maps (SOM), a type of artificial neural network. Though it is a visualization and clustering tool, the researchers want to probe on its ability to detect outliers and properly classify a newborn as normal or not by coming up with a statistically computed threshold value. Instead of working directly with the original attributes of the data, a reduced set of SOM prototypes is utilized to represent the data in a space of smaller dimension, seeking to preserve the probability distribution and topology of the input space. Results showed a misclassification rate of 13.5%. Though it is found to be slightly less superior to the existing classification rules, the proposed methodology was able to address the problem of finding a statistical threshold value. Also, the methodology verifies that age has a major effect on misclassifying “Normal” as “Abnormal” since postponement of newborn screening to a later age causes the quantization error to boost drastically, hence, easily exceeding the value of the first decision threshold.
Keywords: self-organizing kohonen maps (SOM), classification algorithm, outlier detection, newborn screening for congenital hypothyroidism
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Year: 2013 Vol.: 62 No.: 2
Record ID: 42 [ Page 2 of 2, No. 19 ]
Career opportunities in the pharmaceutical industry
Authors: Jennifer Ly
Abstract:
Keywords:
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Year: 2013 Vol.: 62 No.: 1
Record ID: 41 [ Page 2 of 2, No. 20 ]
An elementary proof of independence of least squares estimation of regression coefficients and of variance in linear regression
Authors: Alexaander R. De Leon; Joyce Raymund B. Punzalan
Abstract:
Keywords:
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Year: 2013 Vol.: 62 No.: 1
Record ID: 40 [ Page 2 of 2, No. 21 ]
High dimensional nonparametric discrete choice model
Authors: Maureen Dinna D. Giron
Abstract:
The functional form of a model can be a constraint in the correct prediction of discrete choices. The fl exibility of a nonparametric model can increase the likelihood of correct prediction. The likelihood of correct prediction of choices can further be increased if more predictors are included, but as the number of predictors approaches or exceeds the sample size, more serious complications can be generated than the improvement in prediction. With high dimensional predictors in discrete choice modeling, we propose a generalized additive model (GAM) where the predictors undergo dimension reduction prior to modeling. A nonparametric link function is proposed to mitigate the deterioration of model fi t as a consequence of dimension reduction. Using simulated data with the dependent variable having two or three categories, the method is comparable to the ordinary discrete choice model when the sample size is suffi ciently large relative to the number of predictors. However, when the number of predictors exceeds substantially the sample size, the method is capable of correctly predicting the choices even if the components included in the model account for only 20% of the total variation in all predictors.
Keywords: discrete choice model; generalized additive model; high dimensional data; nonparametric model
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Year: 2013 Vol.: 62 No.: 1
Record ID: 39 [ Page 2 of 2, No. 22 ]
Esstimation under purposive sampling with auxiliary variable
Authors: John Erwin Banez
Abstract:
A PPS purposive selection and estimation was studied. A purposive sampling proposed by Guarte and Barrios (2006) is used. Instead of SRS, PPS with auxiliary variable was used in the selection. Results were compared to SRS estimates and SRS purposive. Standard error and coefficient of variation of estimates were basis for comparison. It was shown that PPS purposive has comparable results with SRS purposive and both have better performance compared to SRS.
Keywords: purposive sampling; bootstrap; auxiliary variable
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Year: 2013 Vol.: 62 No.: 1
Record ID: 38 [ Page 2 of 2, No. 23 ]
Sparse principal component regression
Authors: Joseph Ryan G. Lansangan
Abstract:
Modeling of complex systems is usually confronted with high dimensional independent variables. Econometric models are usually built using time series data that often exhibit nonstationarity due to the impact of some policies and other economic forces. Both cases are usually affected by the multicollinearity problem resulting to unstable least squares estimates of the linear regression coefficients. Principal component regression can provide solution, but in cases where the regressors are nonstationary or the dimension exceeds the sample size, principal components may yield simple averaging of the regressors and the resulting model is difficult to interpret due to biased estimates of the regression coefficients. A sparsity constraint is added to the least squares criterion to induce the sparsity needed for the components to reflect the relative importance of each regressor in a sparse principal component regression (SPCR) model. Simulated and real data are used to illustrate and assess performance of the method. SPCR in many cases leads to better estimation and prediction than conventional principal component regression (PCR). SPCR is able to recognize relative importance of indicators from the sparse components as predictors. SPCR can be used in modeling high dimensional data, as an intervention strategy in regression with nonstationary time series data, and when there is a general problem of multicollinearity.
Keywords: sparsity; high dimensionality; multicollinearity; nonstationarity; sparse principal components
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Year: 2013 Vol.: 62 No.: 1
Record ID: 37 [ Page 2 of 2, No. 24 ]
Value-at-risk measures for the PSE index using hidden markov models
Authors: Joselito C. Magadia
Abstract:
VaR measures for the PSE index are estimated using an m-state normal-hidden Markov model. The estimation procedure will be done under an unconditional approach and a conditional approach. Backtesting will be done to assess how well the estimates performed.
Keywords: homogeneous, irreducible, aperiodic Markov Chain
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Year: 2013 Vol.: 62 No.: 1
Record ID: 36 [ Page 2 of 2, No. 25 ]
Bootstrap estimation of the average household expenditure on personal care and effects of regional level
Authors: Jachelle Anne G. Dimapilis
Abstract:
This study explored the use of bootstrap in estimating households’ expenditure on personal care and effects at regional level. Bootstrap yields superior estimates compared to the estimates of simple random sampling without replacement (SRSWOR). Bootstrap estimates have lower variance than SRSWOR estimates. Bootstrap estimates have smaller percentage difference from the actual mean compared with SRSWOR estimates.
Keywords: survey sampling; predictive estimation; bootstrap estimation
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Year: 2013 Vol.: 62 No.: 1
Record ID: 35 [ Page 2 of 2, No. 26 ]
Nonparametric transfer function model with localized temporal effect
Authors: John Carlo P. Daquis
Abstract:
A semiparametric transfer function model is proposed and estimated using the backfitting algorithm. Simulation studies indicated that the procedure provides robust estimates for the transfer function especially for short time series data. This provides a viable alternative to the parametric transfer function model that requires large number of time points to estimate a number of parameters of the model. Furthermore, in the presence of seasonality or structural change, the procedure generally yields more robust estimates of the transfer function model than the maximum likelihood estimates of the parameters of the parametric model.
Keywords: transfer function model; semiparametric model; backfitting; mixed models
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Year: 2013 Vol.: 62 No.: 1
Record ID: 34 [ Page 2 of 2, No. 27 ]
Sampling from a Skewed Population: The Sampling Design of the 2011 Survey of Enterprises in the Philippines
Authors: Erniel B. Barrios
Abstract:
Keywords:
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Year: 2012 Vol.: 61 No.: 2
Record ID: 33 [ Page 2 of 2, No. 28 ]
Robust Methods in Time Series Models with Volatility
Authors: Wendell Q. Campano
Abstract:
Volatility in time series data is often accounted into the model by postulating a conditionally heteroskedastic variance. In-sample prediction maybe satisfactory but the out-sample prediction is usually problematic. A test for presence of volatility through a nonparametric method is proposed. An estimation procedure for the stationary part of the model by integrating block bootstrap and AR-sieve into the forward search algorithm is also provided. Simulation studies indicated high power for the nonparametric procedure in detecting local volatilities. On the other hand, the estimation method generated robust estimates of the parameters of the time series model in the presence of temporary volatility.
Keywords: block bootstrap; AR-sieve; forward search algorithm; nonparametric test; volatility
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Year: 2012 Vol.: 61 No.: 2
Record ID: 32 [ Page 2 of 2, No. 29 ]
Poisson Spatial Autoregression Modelling of Poverty Count Data in the Philippines
Authors: John Erwin S. Banez
Abstract:
Count data with skewed distribution and possible spatial autoregression (SAR) often causes difficulty in modelling. Violations on the assumptions in ordinary least squares (OLS) may occur. While Poisson regression can offer some remedy in modelling count data, it still does not take into account the spatial dependencies of the data. This paper uses general linear estimation via backfitting algorithm in Poisson-SAR of poverty count in the Philippines for 2000. The model is assessed based on comparison from other models and the actual poverty count (MAPE and poverty map). MAPE was lowest in Poisson-SAR compared to other models.
Keywords: spatial autoregression; backfitting algorithm; poisson regression
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Year: 2012 Vol.: 61 No.: 2
Record ID: 31 [ Page 2 of 2, No. 30 ]
Nonparametric Bootstrap Estimation of the Population Ratio Using Ranked Set Sampling
Authors: Kevin Carl P. Santos; Charisse Mae I. Castillo; Reyna Belle d.S. de Jesus; Nina B. Telan; Crystal Angela P. Vidal
Abstract:
Ranked Set Sampling (RSS) yields unbiased and more reliable estimators of the population mean and proportion while keeping low costs. Using nonparametric bootstrap estimation, the efficiency of the ratio estimates using RSS with Simple Random Sampling (SRS) are compared. A simulation study accounting for the sampling rate, population size, population variance and correlation with the concomitant variable was conducted to compare RSS and SRS in estimating ratios. When ranking was done on the numerator characteristic, RSS generally performs better than SRS in terms of their relative bias. Likewise, in terms of precision, RSS generally produces better estimates when ranking was done on the numerator characteristic. On homogeneous populations, contrary to what was expected, RSS performed better over SRS. On heterogeneous populations, on the other hand, the two sampling designs are generally comparable
Keywords: population ratio; ranked set sampling; simple random sampling; nonparametric bootstrap estimation
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Year: 2012 Vol.: 61 No.: 2
Record ID: 30 [ Page 2 of 2, No. 31 ]
Analysis of Mother's Day Celebration Via Circular Statistics
Authors: Ali H. Abuzaid
Abstract:
This paper handles with much emphasis mother's day celebration around the world a day that is celebrated on various days in different countries. These days are marked in relation to certain historical, religious or mythical events for every nation. The celebration of mother's day by 152 nations is analyzed using a set of circular statistics procedures to study its characteristics. The frequencies of celebration days are modeled, possible clusters and outliers are defined to assess possible factors that may affect the celebration in a certain date. These factors are found to be culture, language, colonization and neighborhood with insignificant role of religion.
Keywords: boxplot; cluster; direction; outlier
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Year: 2012 Vol.: 61 No.: 2
Record ID: 29 [ Page 2 of 2, No. 32 ]
Purposive Sampling as an Optimal Bayes Sampling Design
Authors: Jacqueline M. Guarte
Abstract:
Purposive sampling takes place when the researcher’s knowledge about the population is used to handpick the units to be included in the sample. This is hinged on the experienced researcher’s belief that the handpicked sampling units will provide “enough” information to characterize the population. Bayesian analysis makes explicit use of prior information as part of the model to satisfy some optimality criteria. Hence, purposive rather than purely random locations of design points need to be chosen. This paper presents a proof that purposive sampling is an optimal Bayes sampling design. Purposive sampling satisfies the sufficient condition for an optimal Bayes sampling design set by Zacks (1969) for single-phase designs. It is shown that the posterior Bayes risk of the population parameter ? given the sample observations is independent of the observed values under purposive sampling. The parameter of interest is the population mean. The normal distribution is used for the sampling distribution and the prior distribution of the population mean due to its universal significance and mathematical maneuverability. The squared error loss function is used in determining the posterior Bayes risk associated with estimating the population mean, with the sample mean as estimator. The posterior Bayes risk under simple random sampling is also determined for comparison purposes. It is shown that the risk levels under purposive sampling are lower than those under simple random sampling when important model parameters are made to vary.
Keywords: purposive sampling; optimal Bayes sampling design; posterior Bayes risk
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Year: 2012 Vol.: 61 No.: 2
Record ID: 28 [ Page 2 of 2, No. 33 ]
Small Area Estimation with a Multivariate Spatial-Temporal Model
Authors: Arturo M. Martinez, Jr
Abstract:
A multivariate generalization of a spatial-temporal is postulated and used in model-based small area estimation where small area information is borrowed from other units through spatial and temporal correlations. An estimation procedure that combined the backfitting algorithm, AR-sieve bootstrap and Lorenz curve parameterization is proposed. The procedure is illustrated using data on mean per capita income quintiles of households in the Philippines with provincial unit of analysis. The generation of unit-record synthetic household income is feasible even if modeling is done at the provincial level. Estimates of poverty indices based on the synthetic unit-record data generated from the multivariate spatial-temporal model are more reliable than the direct survey estimates. There are only small deviations between the model-based and direct survey estimates of poverty indices at the domain level that validates the accuracy of the model-based small area estimates generated from the multivariate spatial-temporal model.
Keywords: backfitting algorithm; AR-sieve bootstrap; Lorenz curve parameterization; poverty index
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Year: 2012 Vol.: 61 No.: 2
Record ID: 26 [ Page 2 of 2, No. 34 ]
On the Misuse of Slovin's Formula
Authors: Jeffry J. Tejada; Joyce Raymond B. Punzalan
Abstract:
In a number of research studies involving surveys, the so-called Slovin's formula is used to determine the sample size. Unfortunately, many of these studies use the formula inappropriately, giving the wrong impression that it can be used in just about any sampling problem. This paper provides a careful examination of the formula, showing that it is applicable only when estimating a population proportion and when the confidence coefficient is 95%. Moreover, it is optimal only when the unknown population proportion is believed to be close to 0.5.
Keywords: Slovin�s formula; sample size; margin of error
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Year: 2012 Vol.: 61 No.: 1
Record ID: 25 [ Page 2 of 2, No. 35 ]
Ranked Set Sampling
Authors: Kevin Carl P. Santos
Abstract:
Keywords:
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Year: 2012 Vol.: 61 No.: 1
Record ID: 24 [ Page 2 of 2, No. 36 ]
A Multivariate Probit Analysis on the Factors Influencing the Adoption of Water Saving Technologies by Rice Farmers in Sto. Domingo, Nueva Ecija
Authors: Daniel R. Raguindin; Eiffel A. De Vera
Abstract:
We study the adoption of rice farmers of some water saving technologies (WST) such as controlled irrigation, direct seeding, land leveling and aerobic rice system. A multivariate probit model for the adoption of each WST is constructed since usage of different technologies exhibit correlation. The significant factors that influence the WST adoption are education, experience in rice farming, family income of the farmers, and size of manpower involved in farming. Higher education is needed to enhance the ability to successfully implement the WST. Experience in rice farming, i.e., the number of years a farmer is involved in rice management and production, increases the likelihood of adoption among farmers. Farmers with high income have lower likelihood of adoption since the production system in place is already efficient. Furthermore, a farmer is more likely to adopt the technology as more manpower is involved in the production system. The estimated model indicated that the probability of adoption of controlled irrigation is higher than the other three WST. In addition, the adopters of WST had greater output in terms of the harvested rice.
Keywords: water saving technology; multivariate probit model; univariate probit model
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Year: 2012 Vol.: 61 No.: 1
Record ID: 23 [ Page 2 of 2, No. 37 ]
Sampling with Probability Proportional to Aggregate Size Using Nonparametric Bootstrap in Estimating Total Production Area of Top Cereals and Root Crops Across Philippine Regions
Authors: Maria Sofia A. Poblador; Iris Ivy M. Gauran
Abstract:
Cereal and root crop production are of primary interest to the country’s agricultural industry. The need to obtain reliable estimates of total area of production is therefore crucial. This paper examines the Sampling with Probability Proportional to Aggregate Size (PPAS) in terms of unbiasedness and precision of estimates as compared to two known sampling designs, Simple Random Sampling without Replacement (SRSWOR) and Sampling with Probability Proportional to Size Without Replacement (PPSWOR). Among several crops included in the 2002 Philippine Census of Agriculture, rice and corn are considered for cereals, while cassava and sweet potato for root crops. Crop area, which is believed to be highly correlated with total production area, is utilized as auxiliary information. Estimates of total production area are obtained under 1%, 5% and 10% sampling rates. To be able to evaluate precision of PPAS estimates, nonparametric bootstrap variance estimation is performed. It was found out that PPAS estimates are generally better than the two other sampling designs when it comes to precision but almost at par when it comes to unbiasedness.
Keywords: probability proportional to aggregate size sampling; probability proportional to size sampling; simple random sampling; nonparametric bootstrap estimation
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Year: 2012 Vol.: 61 No.: 1
Record ID: 22 [ Page 2 of 2, No. 38 ]
Econometric Modeling of Panel Data on the Saving Patterns of Philippine Agricultural Households
Authors: Angelo M. Alberto; Lisa Grace S. Bersales
Abstract:
This study aims to identify significant determinants of Philippine agricultural household saving using aggregate (regional) household panel data from the Family Income and Expenditure Survey (FIES) (1991 to 2006). Two definitions of saving are used - with and without expenses on durable goods as expenditure item. Guided by analyses using fixed effects models for panel data, the study identifies age of household head, self-employment of household head, land distribution, and young dependency rate as significant determinants of agricultural household saving. Self-employment, however, is significant only when expenses on durable goods is considered as an expenditure item. Also, time and cross-section fixed effects suggest that there are certain years and regions which had less agricultural household saving.
Keywords: panel data; fixed effects; saving rate; agricultural household
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Year: 2012 Vol.: 61 No.: 1
Record ID: 21 [ Page 2 of 2, No. 39 ]
Classification of Congenital Hypothyroidism in Newborn Screening Using Self-Organizing Maps
Authors: Iris Ivy M. Gauran; Maria Sofia Criselda A. Poblador
Abstract:
Each day, the Newborn Screening Reference Center (NSRC) of the National Health Institute in University of the Philippines Manila collects measurements from five attributes to determine whether Congenital Hypothyroidism (CH) is present in a neonate. Detecting the CH cases is a major concern of medical practitioners because it provides richer information than the healthy ones. However, because of the rarity of this metabolic condition, existing classification algorithms oftentimes misclassify a newborn as “normal” even if it is not. This paper investigates the efficiency of Self-Organizing Kohonen Maps (SOM), a type of artificial neural network. Though it is widely known as a tool for visualization and clustering, the researchers want to probe on its ability as a tool for classification, particularly in detecting outliers. Results show that a lower misclassification rate yields from a self-organizing map with higher learning rate and larger training sample size. A bootstrap estimate of the variability of the misclassification error of roughly around 5% is also obtained. The misclassification error rate is lower when the original validation sample is used, compared to the average misclassification error rate computed from the bootstrap validation samples. Particularly, for a learning rate of 0.8 and a ratio of 2:1 training to validation sample, a 2.04% misclassification against 7.93% misclassification with 4.86% standard deviation is observed.
Keywords: self-organizing kohonen maps (SOM); classification algorithm; outlier detection; newborn screening for congenital hypothyroidism
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Year: 2012 Vol.: 61 No.: 1
Record ID: 20 [ Page 2 of 2, No. 40 ]
In a number of research studies involving surveys, the so-called Slovin's formula is used to determine the sample size. Unfortunately, many of these studies use the formula inappropriately, giving the wrong impression that it can be used in just about an
Authors: Lara Paul D. Abitona; Zita VJ Albacea
Abstract:
This paper aims to present methodologies in estimating the number of Vitamin A defi cient children aged six months to fi ve years in the Philippine provinces. Data from the 6th National Nutrition Survey (NNS), specifi cally, the data on plasma retinol which is used to directly determine Vitamin A defi ciency is used to compare direct and model-based methods. The direct estimates obtained was used as the dependent variable while the 2000 Census of Population and Housing and 2002 Field Health Service Information System were used as sources of auxiliary variables in the Poisson regression fi tted using robust standard errors which resulted to a model with Pseudo-R2 of 55.57%. Measures of precision and reliability were also obtained to assess the properties of the estimates for the provincial estimates. In direct estimation technique, 71 provinces have valid estimates but the coeffi cient of variations are all greater than 20%. On the other hand, valid model-based estimates using Poisson regression were observed for 72 provinces, but the coeffi cient of variations are at most 10% for 78% of these provinces. The use of Poisson regression based model generated more precise estimates of the number of children with Vitamin A defi ciency for the provinces.
Keywords: Small area estimation; Poisson regression model
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Year: 2012 Vol.: 61 No.: 1
Record ID: 19 [ Page 2 of 2, No. 41 ]
Assessing Strength of Seasonality Through Sample Entropy: A Simulation Study
Authors: John Carlo P. Daquis; Maria Lizeth M. Laus; Nikki E. Supnet
Abstract:
This paper investigates the behaviour of sample entropy when used as a measure of seasonality of time series. Sample entropy decreases when the series becomes less complex or when regular patterns emerge. The more regular patterns in seasonal data compared to those of non-seasonal data is used in providing evidence that sample entropy is inversely related to the likelihood that seasonality exists in the data. A simulation study was conducted to assess the behaviour of the sample entropy in relation to seasonality. Sample entropy yields large values for time series without seasonality, and as the extent of seasonality becomes dominant, the value decreases. The sample entropy becomes a more reliable measure of seasonality as the length of the time series increases.
Keywords: entropy; sample entropy; seasonality; time series
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Year: 2012 Vol.: 61 No.: 1
Record ID: 27 [ Page 2 of 2, No. 42 ]
Statistical Models for Extreme Values
Authors: Peter Julian A. Cayton
Abstract:
Keywords:
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Year: 2012 Vol.: 61 No.: 1
Record ID: 18 [ Page 2 of 2, No. 43 ]
Sample Sizes to Compare Two Poisson Rates
Authors: Edsel A. Pena
Abstract:
In this note, procedures for determining the sample sizes needed to compare the rates of two Poisson populations to achieve a pre-specified power at a given ratio of the rates are proposed. The first method relies on a conditional uniformly most powerful test (CUMPT) which leads to sample sizes that will guarantee the desired power, but at the cost of using more units than necessary. The second method relies on a normal approximation and may not always guarantee that the desired power will be achieved, but generally yields a power close to the pre-specified value and prescribes smaller sample sizes than the CUMPT-based method. Properties of the procedures are examined using simulation studies. The particular applicability and motivating situations leading to these procedures are in colon cancer research. Illustrations of the applicability of the procedures in studies dealing with tumor counts in mice are presented.
Keywords: conditional uniformly most powerful test; normal approximation test; power function; test function.
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Year: 2012 Vol.: 61 No.: 1
Record ID: 17 [ Page 2 of 2, No. 44 ]
Bootstrap Methods
Authors: Erniel B. Barrios
Abstract:
Keywords:
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Year: 2011 Vol.: 60 No.: 1
Record ID: 16 [ Page 2 of 2, No. 45 ]
A Dose of Business Intelligence: Data Mining
Authors: Joseph Ryan G. Lansangan,
Abstract:
Keywords:
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Year: 2011 Vol.: 60 No.: 1
Record ID: 15 [ Page 2 of 2, No. 46 ]
Copula-Based Vector Autoregressive Models for Bivariate Cointegrated Data
Authors: Hideaki Taima; Ana Maria L. Tabunda,
Abstract:
The copula method is well applied in finance and actuarial science but its application in economic studies is limited and its use in the cointegration framework virtually nil. This paper explores the use of copula method to analyze the remaining dependence after a cointegration relationship is modeled. Specifically, simulated data is used to characterize the behavior of the dependence parameter estimates of several copulas fitted to the distribution of the residuals after cointegrated Vector Autoregressive (VAR) and Vector Error-Correction Mechanism (VECM) models are fitted, as well as evaluate the forecasting ability of the copula-based models. The Clayton, Frank, Gaussian, Gumbel and Plackett copulas are used and are compared on the basis of bias, root mean square error (RMSE) and maximum likelihood. The density forecasting ability of the copula-based VAR and VECM is then compared with that of standard models via conditional Kullback-Leibler Information Criterion (KLIC) divergence measure using simulated and empirical data. The simulation results indicate that the copula-based models generally have better density forecasting ability than standard VAR and VECM models, a finding that is supported in the application of a copula-based VAR to empirical data.
Keywords: Copula; Cointegration; VAR; VECM
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Year: 2011 Vol.: 60 No.: 1
Record ID: 14 [ Page 2 of 2, No. 47 ]
Nearest-Integer Response from Normally-Distributed Opinion (NIRNDO) Model for Likert Scale
Authors: Jonny B. Pornel, Vicente T. Balinas, Giabelle A. Saldaa
Abstract:
This paper proposes that respondents’ opinions on Likert Scale items are normally distributed around their latent ability although their observable responses will be integers in the scale nearest to those opinions. This paper tested the appropriateness of the model on actual data gathered by a Likert scale developed to measure attitude of teachers towards research undertaking. The paper then proceeded to test the soundness of common research practice of using mean and standard deviation to estimate the respondents’ latent ability. The results show that the NIRNDO model could be used appropriately to model responses on Likert scale. Also, the results show that using the mean response to a Likert scale, the resulting 95% confidence interval (mean + 1.96 SEM) would be effective at least 90% of the time. This effectiveness is guaranteed for latent ability in the optimum range [u+0.8, v-0.8] where u and v is the lowest and highest points of the scale.
Keywords: Likert Scale; NIRNDO Model; latent ability
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Year: 2011 Vol.: 60 No.: 1
Record ID: 13 [ Page 2 of 2, No. 48 ]
Substance Use Among Serious Adolescent Offenders Following Different Patterns of Antisocial Activity
Authors: Michelle Besana; Edward P. Mulvey
Abstract:
The present study examines individual differences in the levels of substance use in a sample (n=1,067) of male serious adolescent offenders following distinct trajectories of criminal offending over a three (3) year period. The levels of substance use are compared for the different offender groups controlling the effects of age, ethnicity, and diagnosis of previous drug and alcohol abuse/dependence. The association between antisocial activity and the level of substance use was also examined and compared for the different groups after controlling the effect of institutional placement. The growth or decline in substance use was investigated and compared for the different groups above and beyond the effects of antisocial activity and institutional confinement. After fitting a series of hierarchical generalized linear models for repeated measurements data, results revealed that significant differences in the level of substance use exist among the different offender groups in the sample. Antisocial activity is associated with the level of substance use over time after controlling the effect of institutional placement in all offender groups. Above and beyond the effect of antisocial activity and institutional placement, substance use is increasing over the data collection period in all groups, but the rate of growth is highest in the lowest offending group.
Keywords: hierarchical generalized linear models; growth curve models; substance use; antisocial activity; delinquency; serious adolescent offenders
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Year: 2011 Vol.: 60 No.: 1
Record ID: 12 [ Page 2 of 2, No. 49 ]
Food Inflation, Underemployment and Hunger Incidence: A Vector Autoregressive (VAR) Analysis
Authors: Dennis S. Mapa; Fatima C. Han; Kristine Claire O. Estrada
Abstract:
The high level of hunger incidence in the country is perhaps one of the most pressing issues that need to be addressed by our policy makers. Official government statistics and data from self-rated hunger surveys show an increasing trend in hunger incidence among Filipino households. Data from National Statistical Coordination Board (NSCB) show that the percentage of Filipinos experiencing hunger almost remained the same, decreasing only slightly from 11.1 percent in 2003 to 10.8 percent in 2009. The Social Weather Stations (SWS) quarterly surveys on hunger incidence also show an increasing trend in the percentage of families that experienced hunger, reaching an alarming level of 24 percent in December 2009, representing about 4.4 million households. One probable cause of the increasing trend in hunger is the rising food prices akin to what the country experienced in 2008. This paper aims to determine the impact of food inflation and underemployment on hunger incidence in the Philippines, using the hunger incidence data from the SWS. A vector autoregressive (VAR) model is used to determine the effect of a shock or increase to food inflation and underemployment on total involuntary hunger. Results show that an increase in food prices at the current quarter will increase hunger incidence for five quarters. Shocks to underemployment will also increase hunger incidence but the effects last for only two quarters. The results of this study provide relevant information that will be useful in crafting policies related to the Hunger Mitigation Program of the government.
Keywords: hunger; food inflation; underemployment; vector autoregressive
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Year: 2011 Vol.: 60 No.: 1
Record ID: 11 [ Page 2 of 2, No. 50 ]
Length of a Time Series for Seasonal Adjustment: Some Empirical Experiments
Authors: Lisa Grace S. Bersales
Abstract:
Use of 5 to 15 years of quarterly or monthly data is suggested when doing seasonal adjustment using X11 and its variants. This is meant to address changes in the structure of the time series. Philippine time series are good candidates for this practice since they usually exhibit frequent changes in patterns. Empirical validation of the suggested length of series is done for seasonal ARMA processes. Different quarterly series were simulated for the following situations and seasonal adjustment was done for various lengths of time series: (1) processes without any structural change; (2) processes with abrupt permanent change in structure; (3) processes with gradual permanent change in structure. For all types of processes, both weak and strong seasonality were considered. Regression models were used in testing the effect of length of series used in seasonal adjustment to the error in estimating the seasonal factor. Results show that the length of series used does not have significant effect on the seasonal adjustment for processes without structural change and with abrupt permanent structural change. On the other hand, for processes with gradual permanent change, use of longer lengths of series for seasonal adjustment is better.
Keywords: seasonal adjustment, seasonal factor, X11-ARIMA, seasonal ARMA processes
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Year: 2011 Vol.: 60 No.: 1
Record ID: 10 [ Page 2 of 2, No. 51 ]
Investigating the Efficiency of Stratified Ranked Set Sampling Using Nonparametric Bootstrap Estimation
Authors: Kevin Carl P. Santos; Jenniebie Salagubang
Abstract:
This paper aims to compare stratified random sampling and stratified ranked set sampling. A simulation study is conducted to evaluate the performance of the parameter estimates on both sampling techniques. Population sizes, sampling rates, stratum sizes, and correlation of the target variable and concomitant variable were varied, nonparametric bootstrap was then used in estimating the mean and its standard error. The coefficient of variation (CV) and the bias of the bootstrap estimates were compared. Stratified ranked set sampling generally outperforms stratified random sampling in terms of bias most especially for small populations. The two sampling designs were used in estimating the average mango production per barangay in the country.
Keywords: ranked set sampling; nonparametric bootstrap estimation; stratification; simple random sampling
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Year: 2011 Vol.: 60 No.: 1
Record ID: 9 [ Page 2 of 2, No. 52 ]
Nonparametric Model-Based Predictive Estimation in Survey Sampling
Authors: April Anne H. Kwong
Abstract:
A nonparametric model-based estimator of the population total is proposed. The sample data along with the auxiliary information are used in fitting a generalized additive model that is then used in reconstructing the unknown population. The estimates of the population parameters are computed from the predicted population values (for the unsampled part of the population) and the sample values. A simulation study designed to account for different association patterns between the target variable and the auxiliary variable, population size, and sample size was conducted to evaluate the proposed procedure. The method is robust to data-generating model form, population size, and sampling rate, and is generally superior to design-unbiased estimators.
Keywords: model-based estimation; predictive estimation; nonparametric regression; additive model
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Year: 2011 Vol.: 60 No.: 1
Record ID: 8 [ Page 2 of 2, No. 53 ]
Teaching of Statistical Consulting in the Philippines
Authors: Erniel B. Barrios
Abstract:
Teaching in a developing country is generally challenging due to the inadequate infrastructures in the development of teaching materials and the facilities in the delivery of such. The teacher has to be creative enough in developing cost-effective teaching materials, efficiently allocating the limited resources. Statistical consulting is generally taught using audio-visual infrastructure support. In the Philippines, it has to be taught through case studies, coaching and guided practice to complement the absence or inadequate audio-visual facilities. The methods are fairly adequate in imparting to the students the techniques and necessary skills in the practice of statistical consulting.
Keywords: statistical consulting; statistics education
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Year: 2010 Vol.: 59 No.: 1
Record ID: 7 [ Page 2 of 2, No. 54 ]
Teaching Experiments for a Course in Introductory Statistics
Authors: Josefina V. Almeda
Abstract:
Appreciation of college students on the statistical science relies to a large extent on how the introductory course is managed. Two groups of students (undergraduate statistics majors and non-statistics majors) were exposed to teaching an introductory course. Within each group, half is exposed to fun games intended as enrichment activities, the other half served as the control. Grades after one semester were analyzed and treatment effect is computed through Heckmans’ Selection Model. While the treatment (games) is beneficial for the non-statistics majors, it is disadvantageous for the statistics majors. For students with inherent interest in statistics, the introductory course will only require a clear presentation of concepts that will help them appreciate the discipline. However, the non-statistics majors or those with negative perception on statistics, fun activities like games can help conceal their dislike for statistical science and help improve the eventual outcomes in the course.
Keywords: fun games; Heckmans’ Selection Model; treatment effect
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Year: 2010 Vol.: 59 No.: 1
Record ID: 6 [ Page 2 of 2, No. 55 ]
The Random Component of the Levy Fractional Brownian Motion: A Rotation-Scale-Reflection-Invariant Random Field
Authors: Jeffry J. Tejada
Abstract:
This paper shows that the Levy fractional Brownian motion (LFBM) on the plane factors as a product of two components, one being a deterministic trend term and the other being a rotation-scale-reflection-invariant (RSRI) random field. An important consequence of this characterization is that one can study the LFBM by establishing the properties of the associated RSRI random field.
Keywords: Levy fractional Brownian motion; rotation-scale-reflection-invariant random field
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Year: 2010 Vol.: 59 No.: 1
Record ID: 5 [ Page 2 of 2, No. 56 ]
Backfitting Estimation of a Response Surface Model
Authors: Jhoanne Marsh C. Gatpatan
Abstract:
The backfitting algorithm is used in estimating a response surface model with covariates from a data generated through a central composite design. Backfitting takes advantage of the orthogonality generated by the central composite design on the design matrix. The simulation study shows that backfitting yield estimates and predictive ability of the model comparable to that from ordinary least squares when the response surface bias is minimal. Ordinary least squares estimates generally fails when the response surface bias is large, while backfitting exhibits robustness and still produces reasonable estimates and predictive ability of the fitted model. Orthogonality facilitates the viability of assumptions in an additive model where backfitting is an optimal estimation algorithm.
Keywords: backfitting; response surface model; second order model; central composite design
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Year: 2010 Vol.: 59 No.: 1
Record ID: 4 [ Page 2 of 2, No. 57 ]
Knowledge, Attitudes, and Practices of Filipino Households in Relation to Avian Influenza: A Pilot Study
Authors: Josefina V. Almeda; Jonathan G. Yabes
Abstract:
Avian influenza, commonly known as bird flu, once threatened the lives of birds as well as of human beings. Caused by a virus, it is considered endemic in many parts of Indonesia and Vietnam and in some parts of Cambodia, China, and Thailand. Outbreak of many similar diseases can easily reach epidemic level because households have minimal knowledge of precautionary measures. This study conducted a survey of Filipino households’ knowledge, attitudes, and practices related to avian influenza. Results of this assessment can help in the development of intervention strategies for the mitigation of the hazards such outbreaks may cause to humans. The results of the knowledge, attitudes, and practices study is beneficial to the country since it showed that there should be an improvement on the publics’ knowledge of transmission and preventive measure and that health professionals and other concerned agencies should provide effective information to prevent the disease.
Keywords: knowledge; attitudes; practices; avian influenza
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Year: 2010 Vol.: 59 No.: 1
Record ID: 3 [ Page 2 of 2, No. 58 ]
Loglinear and Classification Tree Models of the Decision Paradigm of the Tuberculosis Diagnostic Committee
Authors: Caryl Rose E. Alfonte
Abstract:
The TB Diagnostic Committees (TBDC) evaluate cases of pulmonary tuberculosis (PTB) symptomatics who are smear-negative, but whose chest x-rays show lesions suggestive of tuberculosis that may warrant anti-TB treatment. In a review of the 600 TBDC referral forms of new patients who consulted at Manila district health centers from 2006 to 2008, the demographic and clinical characteristics associated with a positive chest x-ray and eventually leading to a diagnosis of new active PTB are identified using loglinear models and classification trees.
Keywords: Classification tree, Loglinear model, Positive chest x-ray, Pulmonary tuberculosis, Sputum smear-negative, TB Diagnostic Committee
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Year: 2010 Vol.: 59 No.: 1
Record ID: 2 [ Page 2 of 2, No. 59 ]
Spatio-Temporal Modeling of Growth in Rice Production in the Philippines
Authors: Angela D. Nalica
Abstract:
When the strong El Nino episode in recent history happened in 1998, gross value added of the rice sector in the Philippines declined by as much as 24% while other crops were able to keep the decline to within single digit level. The convergence hypothesis was verified among the Philippine provinces with reference to rice production. Convergence could mean harmonized efforts among various stakeholders to increase production and hopefully aim for food sufficiency. Divergence on the other hand could imply the need for structural assessment of the sector including the goals of various stakeholders, so that an optimal strategy that can stimulate development will be identified. A spatial term is incorporated into the model, providing empirical evidence for the need to localize rice production policy programs across the country. The spatial term also accounts for the natural endowments of the producing provinces that complement those policies in realizing progress in the sector. Rice production among the Philippine provinces diverged in the period 1990-2002. The El Nino episode of 1998 pulled down rice yield by as much as 10% aggravating further the divergence among provinces.
Keywords: spatio-temporal model; backfitting; autoregression; convergence hypothesis; agricultural growth
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Year: 2010 Vol.: 59 No.: 1
Record ID: 1 [ Page 2 of 2, No. 60 ]
What Drives the Dynamic Conditional Correlation of Foreign Exchange and Equity Returns?
Authors: Gregorio A. Vargas
Abstract:
This paper establishes the link of microstructure and macroeconomic factors with the time-varying conditional correlation of foreign exchange and excess equity returns. By using the proposed DCC model with exogenous variables, capital flows and interest rate differentials are shown to be significant determinants of this correlation which is inclusive of the short-run variation of both asset returns. The results also provide evidence of the dynamic behavior of global investors as they seek parity in equity returns between home and foreign markets to reduce exchange rate risks.
Keywords: uncovered equity parity; order flow; DCCX
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Year: 2010 Vol.: 59 No.: 1