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Record ID: 60 [ Page 11 of 16, 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 11 of 16, 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 11 of 16, 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 11 of 16, 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 11 of 16, 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 11 of 16, 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 11 of 16, 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 11 of 16, 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 11 of 16, 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 11 of 16, 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