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Record ID: 100 [ Page 4 of 8, No. 1 ]
Zero-Truncated New Quasi Poisson-Lindley Distribution and its Applications
Authors: Rama Shanker and Kamlesh Kumar Shukla
Abstract:
A zero-truncated new quasi Poisson-Lindley distribution (ZTNQPLD), which includes zero-truncated Poisson-Lindley distribution (ZTPLD) as a particular case, has been studied. Its probability mass function has also been obtained by compounding size-biased Poisson distribution (SBPD) with an assumed continuous distribution. The rth factorial moment of ZTNQPLD have been derived and hence its raw moments and central moments have been presented. The expressions for coefficient of variation, skewness, kurtosis, and index of dispersion have been given and their nature and behavior have been studied graphically. The method of maximum likelihood estimation has been discussed for estimating the parameters of ZTNQPLD. Finally, the goodness of fit of ZTNQPLD has been discussed with some datasets and the fit has been found better as compared with zero truncated Poisson distribution (ZTPD) and zero- truncated Poisson- Lindley distribution (ZTPLD).
Keywords: Zero-truncated distribution, New quasi Poisson-Lindley distribution, compounding, moments, Maximum Likelihood estimation, Goodness of fit.
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Year: 2017 Vol.: 66 No.: 2
Record ID: 99 [ Page 4 of 8, No. 2 ]
Regression and Variable Selection via A Layered Elastic Net
Authors: Michael Van B. Supranes and Joseph Ryan G. Lansangan
Abstract:
One approach in modeling high dimensional data is to apply an elastic net (EN) regularization framework. EN has the good properties of least absolute shrinkage selection operator (LASSO), however, EN tends to keep variables that are strongly correlated to the response, and may result to undesirable grouping effect. The Layered Elastic Net Selection (LENS) is proposed as an alternative framework of utilizing EN such that interrelatedness and groupings of predictors are explicitly considered in the optimization and/or variable selection. Assuming groups are available, LENS applies the EN framework group-wise in a sequential manner. Based on the simulation study, LENS may result to an ideal selection behavior, and may exhibit a more appropriate grouping effect than the usual EN. LENS results to poor prediction accuracy, but applying OLS on the selected variables may yield optimum results. At optimal conditions, the mean squared prediction error of OLS on LENS-selected variables are on par with the mean squared prediction error of OLS on EN-selected variables. Overall, applying OLS on LENS-selected variables makes a better compromise between prediction accuracy and ideal grouping effect.
Keywords: regression, variable selection, variable clustering, high dimensional data, elastic net, grouping effect
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Year: 2017 Vol.: 66 No.: 2
Record ID: 98 [ Page 4 of 8, No. 3 ]
Asymptotic Decorrelation of Discrete Wavelet Packet Transform of Generalized Long-memory Stochastic Volatility
Authors: Alex C. Gonzaga
Abstract:
We derive the asymptotic properties of discrete wavelet packet transform (DWPT) of generalized long-memory stochastic volatility (GLMSV) model, a relatively general model of stochastic volatility that accounts for persistent (or long-memory) and seasonal (or cyclic) behavior at several frequencies. We derive the rates of convergence to zero of between-scale and within-scale wavelet packet coefficients at different subbands. Wavelet packet coefficients in the same subband can be shown to be approximately uncorrelated by appropriate choice of basis vectors using a white noise test. These results may be used to simplify the variance-covariance matrix into a diagonalized matrix, whose diagonal elements have the least distinct variances to compute.
Keywords: discrete wavelet packet transform, generalized longmemory stochastic volatility, asymptotic decorrelation
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Year: 2017 Vol.: 66 No.: 2
Record ID: 96 [ Page 4 of 8, No. 4 ]
Survival Analysis for Weaning Time of the Palestinian Children
Authors: Ali H. Abuzaid and Raida F. Zaqout
Abstract:
This study addresses the factors that have an effect on the weaning time of the Palestinian children based on the Palestinian family survey data in 2006. It was found that the Weibull parametric model is the most appropriate one to fit the data. The study showed that factors such as child’s weight at birth, child’s age, mother’s age at delivery, and mother’s educational status have significant effects on the weaning time. The findings also revealed that factors such as mother’s refugee status, locality type, total live births, and mother’s smoking status do not have any significant effect at 0.05 level of significance on the duration of breastfeeding.
Keywords: breastfeeding, censored, Cox proportional model, Wald statistic
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Year: 2017 Vol.: 66 No.: 1
Record ID: 95 [ Page 4 of 8, No. 5 ]
Modeling Iloilo River Water Quality
Authors: Michelle B. Besana and Philip Ian P. Padilla
Abstract:
The analysis of covariance model (ANCOVA) with heterogeneous variance first-order autoregressive error covariance structure (ARH1) was used to model the differences in fecal streptococci concentration in Iloilo River over time with fixed site and seasonal effects as primary factors of interest, and water temperature, pH, dissolved oxygen, and salinity as covariates. The restricted maximum likelihood estimation (REML) procedure was used to derive the parameter estimates and the Kenward-Roger adjustment in the degrees of freedom was used to better approximate the distributions of the test statistics. The effect of season was highly significant (p = 0.0019). The site effect was significant at the 0.0539 level. The effects of water surface temperature and pH were significant at the 0.0655 and 0.0828 level, respectively. The effects of dissolved oxygen and salinity were not significant. Although the coefficient of determination was modest, the result of the study is useful in characterizing the dynamics of Iloilo River bacteriological system which contributes to an improved understanding of the Iloilo River water quality.
Keywords: analysis of covariance (ANCOVA), heterogeneous variance first-order autoregressive error covariance structure (ARH1), restricted maximum likelihood estimation (REML), fecal indicator bacteria (FIB), fecal streptococcus
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Year: 2017 Vol.: 66 No.: 1
Record ID: 94 [ Page 4 of 8, No. 6 ]
An Index of Financial Inclusion in the Philippines: Construction and Analysis
Authors: Mynard Bryan R. Mojica and Claire Dennis S. Mapa
Abstract:
Financial inclusion has become a policy priority in many developing countries, including the Philippines. However, the issue of its robust measurement is still outstanding. The challenge comes from the fact that financial inclusion is a multidimensional phenomenon. A comprehensive measure is therefore needed to adequately gauge the inclusiveness of a financial system. This paper constructed a Financial Inclusion Index (FII) to measure access to and usage of financial services in the Philippines using provincial data. Results show that while there are marked geographical disparities based on the FII, there is significant positive spatial autocorrelation indicating that nearby provinces exhibit similar levels of financial inclusion. The paper also showed the relationship between the FII and some variables that are often linked to financial inclusion such as income, poverty, literacy, and employment as well the province’s level of human development and competitiveness. On the methodological side, possible improvements and technical innovations in constructing the FII are laid out to maximize its potential as an analytical tool for surveillance and policy-making.
Keywords: inclusive finance, composite indicator, financial inclusion index
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Year: 2017 Vol.: 66 No.: 1
Record ID: 93 [ Page 4 of 8, No. 7 ]
Comparison of Regression Estimator and Ratio Estimator: A Simulation Study
Authors: Dixi M. Paglinawan
Abstract:
We compared ratio and regression estimators empirically based on bias and coefficient of variation. Simulation studies accounting for sampling rate, population size, heterogeneity of the auxiliary variable x, deviation from linearity and model misspecification were conducted. The study shows that ratio estimator is better than regression estimators when regression line is close to the origin. Ratio and regression estimators still work even if there is a weak linear relationship between x and y, provided that there is minimal, if not absent, model misspecification. When the relationship between the target variable and the auxiliary variable is very weak, bootstrap estimates yield lower bias. Regression estimator is generally more efficient than ratio estimator.
Keywords: auxiliary variable, ratio estimator, regression estimator, bootstrap estimator
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Year: 2017 Vol.: 66 No.: 1
Record ID: 92 [ Page 4 of 8, No. 8 ]
A Class of Ratio-Cum-Product Type Exponential Estimators under Simple Random Sampling
Authors: Gajendra K. Vishwakarma and Sayed Mohammed Zeeshan
Abstract:
In this paper, a class of ratio-cum-product type exponential estimators have been proposed under simple random sampling to estimate the population mean. The proposed class of estimators has been compared with the other existing estimators. We have compared the efficiency of the proposed class of estimators with the other standard estimators and found through empirical study that the previous estimators are inferior to the present proposed class of estimators. The population used in the empirical study are all varying very much from each other and thus it demonstrate the superiority of the present estimators under all type of situation.
Keywords: auxiliary variable, study variable, simple random sampling, ratio type estimators, product type estimators, bias, MSE
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Year: 2017 Vol.: 66 No.: 1
Record ID: 91 [ Page 4 of 8, No. 9 ]
An Exponentially Weighted Moving Average Control Chart for Zero-Truncated Poisson Processes: A Design and Analytic Framework with Fast Initial Response Feature
Authors: Robert Neil F. Leong, Frumencio F. Co, Vio Jianu C. Mojica and Daniel Stanley Y. Tan
Abstract:
Inspired by the capability of exponentially-weighted moving average (EWMA) charts to balance sensitivity and false alarm rates, we propose one for zero-truncated Poisson processes. We present a systematic design and analytic framework for implementation. Further, we add a fast initial response (FIR) feature which ideally increases sensitivity without compromising false alarm rates. The proposed charts (basic and with FIR feature) were evaluated based on both in-control average run length (ARL0) to measure false alarm rate and out-of-control average run length (ARL1) to measure sensitivity to detect unwanted shifts. The evaluation process used a Markov chain intensive simulation study at different settings for different weighting parameters (ω). Empirical results suggest that for both scenarios, the basic chart had: (1) exponentially increasing ARLs as a function of the chart threshold L; and (2) ARLs were longer for smaller ωs. Moreover, the added FIR feature has indeed improved ARL1 within the range of 5% - 55%, resulting to quicker shift detections at a relatively minimal loss in ARL0. These results were also compared to Shewhart and CUSUM control charts at similar settings, and it was observed that the EWMA charts generally performed better by striking a balance between higher ARL0 and lower ARL1. These advantages of the EWMA charts were more pronounced when larger shifts in the parameter λ happened. Finally, a case application in monitoring hospital surgical out-of-controls is presented to demonstrate its usability in a real-world setting.
Keywords: exponentially-weighted moving average control chart, zero-truncated Poisson process, fast initial response feature, average run length, infection control
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Year: 2017 Vol.: 66 No.: 1
Record ID: 90 [ Page 4 of 8, No. 10 ]
Spatial-Temporal Models and Computational Statistics Methods: A Survey
Authors: Erniel B. Barrios and Kevin Carl P. Santos
Abstract:
We introduce panel models and identify its link to spatial-temporal models. Both models are characterized and differentiated through the variance-covariance matrix of the disturbance term. The resulting estimates or tests are as complicated as the nature of the said variance-covariance matrix. Some iterative methods typically used in computational statistics are also presented. These methods are used in conducting statistical inference for spatial-temporal models.
Keywords: panel data, spatial-temporal model, forward search algorithm, additive models, backfitting algorithm, isotonic regression
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Year: 2017 Vol.: 66 No.: 1
Record ID: 89 [ Page 4 of 8, No. 11 ]
A Sustainability Model for Small Health Maintenance Programs
Authors: Mia Pang Rey and Ivy D.C. Suan
Abstract:
The objective of this paper is to present a theoretical model that can assist community-based health maintenance providers in handling their actuarial risk. It determines the factors and conditions under which the said model can be made financially sustainable. The break-even formulas for some of the parameters are derived. It likewise examines the amount of reserves needed to manage underwriting risk.
Keywords: health maintenance programs, sustainability
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Year: 2016 Vol.: 65 No.: 2
Record ID: 88 [ Page 4 of 8, No. 12 ]
Multiple Statistical Tools for Divergence Analysis of Rice (Oryza sativa L.) Released Varieties
Authors: Aldrin Y. Cantila, Sailila E. Abdula, Haziel Jane C. Candalia and Gina D. Balleras
Abstract:
Rice released varieties are genetic resources bulked with good genes. To define the potentials of these germplasm, genetic divergence analysis must be done. The study used different statistical tools such as descriptive statistics, Kolmogorov-Smirnov test, Shannon-Weaver diversity index (H’), correlation statistics (r), principal component analysis (PCA), Dixon’s test and clustering statistics in evaluating 29 NSIC (National Seed Industry Council) released varieties based on 11 morphological traits. Descriptive statistics showed significant differences on the traits used while following a normal distribution. Shannon-Weaver diversity derived a range of 0.55 (number of filled grain per panicle, NFGP) to 0.91 (grain yield, GY and number of tillers, NT) that infer moderate to high diversity traits. Correlation statistics among traits showed a range of r = -0.55 to 0.84 which GY was noted to positively correlate to all traits. PCA accounted 39.95% and 26.10% for PC1 and PC2, respectively. Notable component loading for the yield component traits such as panicle weight (PW) showed the highest contributor of positive projections in two PCs that explained 66.05% of the variation. PCA also detected two latent traits such GY and spikelet fertility (SF) as confirmed in Dixon’s test where outlier was found in SF and to yield contributing traits. Clustering statistics separated varieties into 5 clusters with a range of 5.88 to 106.22 euclidean distance (ED). Among the clusters, 5th cluster composed of one variety, NSIC Rc240 gave the highest GY (7.07 tha-1), NFGP (152.67), one thousand grain weight (24.77 g), PW (5.08 g) and spikelet number per panicle (185.33). The variety could potentially be adapted and a good source of genes for rice improvement localize at General Santos City.
Keywords: clustering statistics, correlation statistics, descriptive statistics, Shannon-Weaver index, rice released varieties, principal component analysis
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Year: 2016 Vol.: 65 No.: 2
Record ID: 87 [ Page 4 of 8, No. 13 ]
Linear Discriminant Analysis vs. Genetic Algorithm Neural Network with Principal Component Analysis for Hyperdimensional Data Analysis: A study on Ripeness Grading of Oil Palm (Elaeis guineensis Jacq.) Fresh Fruit
Authors: Divo Dharma Silalahi, Consorcia E. Reaño, Felino P. Lansigan, Rolando G. Panopio and Nathaniel C. Bantayan
Abstract:
Using Near Infrared Spectroscopy (NIRS) spectral data, the Linear Discriminant Analysis (LDA) performance was compared with the Genetic Algorithm Neural Network (GANN) to solve the classification or assigning problem for ripeness grading of oil palm fresh fruit. The LDA is known as one of the famous classical statistical techniques used in classification problem and dimensionality reduction. The GANN is a modern computational statistical method in terms of soft computing with some adaptive nature in the system. The first four new components variables as result of Principal Component Analysis (PCA) also were used as input variables to increase the efficiency and made the data analysis process faster. Based on the results, both in training and validation phase GANN technique had lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), higher percentage of correct classification and suitable to handle large amount of data compared to the LDA technique. Therefore, the GANN technique is superior in terms of precision and less error rate to handle a hyperdimensional problem for data analysis in ripeness classification of oil palm fresh fruit compared to the LDA.
Keywords: Near Infrared Spectroscopy, Neural Network, Genetic Algorithm, Linear Discriminant Analysis, Principal Component Analysis, Oil Palm, Ripeness
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Year: 2016 Vol.: 65 No.: 2
Record ID: 86 [ Page 4 of 8, No. 14 ]
Quantile and Restricted Maximum Likelihood Approach for Robust Regression of Clustered Data
Authors: May Ann S. Estoy and Joseph Ryan G. Lansangan
Abstract:
Quantile regression and restricted maximum likelihood are incorporated into a backfitting approach to estimate a linear mixed model for clustered data. Simulation studies covering a wide variety of scenarios relating to clustering, presence of outliers, and model specification error are conducted to assess the performance of the proposed methods. The methods yield biased estimates yet high predictive ability compared to ordinary least squares and ordinary quantile regression.
Keywords: linear mixed models; quantile regression; restricted maximum likelihood; backfitting; bootstrap; clustered data
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Year: 2016 Vol.: 65 No.: 2
Record ID: 85 [ Page 4 of 8, No. 15 ]
Nonparametric Hypothesis Testing for Isotonic Survival Models with Clustering
Authors: John D. Eustaquio
Abstract:
A nonparametric test for clustering in survival data based on the bootstrap method is proposed. The survival model used considers the isotonic property of the covariates in the estimation via the backfitting algorithm. Assuming a model that incorporates the clustering effect into the piecewise proportional hazards model, simulation studies indicate that the procedure is correctly-sized and powerful in a reasonably wide range of scenarios. The test procedure for the presence of clustering over time is also robust to model misspecification.
Keywords: Bootstrap confidence interval; Survival Analysis; Clustered Data; Backfitting Algorithm; Generalized Additive Models; Nonparametric bootstrap.
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Year: 2016 Vol.: 65 No.: 2
Record ID: 84 [ Page 4 of 8, No. 16 ]
Semiparametric Probit Model for High-dimensional Clustered Data
Authors: Daniel R. Raguindin and Joseph Ryan G. Lansangan
Abstract:
A semiparametric probit model for high dimensional clustered data and its estimation procedure are proposed. The model is characterized by flexibility in the model structure through a nonparametric formulation of the effect of the predictors on the dichotomous response and a parametric specification of the inherent heterogeneity due to clustering. The predictive ability of the model is further investigated by looking at possible factors such as dimensionality, presence of misspecification, clustering, and response distribution. Simulation studies illustrate the advantages of using the proposed model over the ordinary probit model even in low dimensional cases. High predictive ability is observed in high dimensional cases especially when the distribution of the response categories is balanced. Results show that cluster distribution and functional form of the response variable do not affect the performance of the model. Also, the predictive ability of the proposed estimation increases as the number of clusters increases. Under the presence of misspecification, the predictive ability of the model is slightly lower yet remains better than the ordinary probit model.
Keywords: probit model, high dimensional data, backfitting algorithm, local scoring algorithm
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Year: 2016 Vol.: 65 No.: 2
Record ID: 97 [ Page 4 of 8, No. 17 ]
SPCR-Based Control Chart for Autocorrelated Processes with High Dimensional Exogenous Variables
Authors: Paul Eric G. Abeto and Joseph Ryan G. Lansangan
Abstract:
Monitoring processes in an industry is one means to ensure the quality of goods produced or services provided. Control charts are constructed by estimating control limits wherein the process could be identified as stable. The estimation is made by analyzing the behavior of the monitored process. However, the assumptions of uncorrelatedness and normality of the measurements, common in most control charts, are sometimes uncharacteristic of the monitored process. Also, data from other variables may be available and may provide meaningful information on the behavior of the monitored process, and thus may be valuable in the estimation of the control limits. In this paper, a methodology of using sparse principal component regression from high dimensional exogenous variables to estimate control limits of autocorrelated processes is proposed. Simulations are made to further study different scenarios that may affect the proposed estimation. The false alarm rate, average run length during stable periods, and first detection rate upon structural change are used as key indicators for characterization and/or comparison. Simulation results suggest that modelling a process using high dimensional exogenous variables through sparse principal components creates better estimation of its corresponding control chart parameters. False alarm rates and average run lengths were comparable with the Exponentially Weighted Moving Average (EWMA) control chart. Also, faster identification of structural change was observed potentially due to the fact that the process is modelled in terms of other information carried by the exogenous variables.
Keywords: Control chart, autocorrelated process, high dimensional data, sparse principal component regression
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Year: 2016 Vol.: 65 No.: 2
Record ID: 83 [ Page 4 of 8, No. 18 ]
Small Area Estimation with Spatiotemporal Mixed Model
Authors: Divina Gracia L. Del Prado and Erniel B. Barrios
Abstract:
A spatiotemporal model with nested random effects is proposed for small area estimation where sample data are generated from a rotating panel survey. Two methods of estimation are introduced, integrating the backfitting algorithm and bootstrap procedure in two different approaches. Simulation study shows superior predictive ability of the fitted model. The small area estimation methods also produced efficient estimates of parameters in a wide class of population scenarios. The model-based small area estimation procedure is also better over the design-based approach in estimating unemployment rate from the Philippine Labor Force Survey.
Keywords: spatiotemporal mixed model; small area estimation; backfitting algorithm; bootstrap.
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Year: 2016 Vol.: 65 No.: 2
Record ID: 82 [ Page 4 of 8, No. 19 ]
Interdependence of Philippine Stock Exchange Sector Indices: Evidence of Long-run and Short-run Relationship
Authors: Karl Anton M. Retumban
Abstract:
The interdependence of the Philippine Stock Exchange Sector Indices was analyzed using Johansen’s Cointegration test, Granger-Causality and Forecast Error Variance Decomposition. Daily, weekly and monthly data were used from January 2006 up to June 2015.The results confirm existence of cointegration among the six sector indices implying that the indices follow a common trend and have a long-run relationship. This is true across the daily, weekly and monthly data. There is also a uni-directional causality existing among the sector indices. Aside from the sector indices own shock largely influencing its own variation, the innovations from the financial sector index significantly contributes to the variation of other sector indices.
Keywords: Johansen’s Cointegration, Granger Causality, Forecast Error Variance Decomposition, Philippine Stock Exchange Sector Indices
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Year: 2016 Vol.: 65 No.: 1
Record ID: 81 [ Page 4 of 8, No. 20 ]
Drivers of Household Income Distribution Dynamics in the Philippines*
Authors: Arturo Martinez Jr., Mark Western, Wojtek Tomaszewski, Michele Haynes, Maria Kristine Manalo, and Iva Sebastian
Abstract:
Using counterfactual simulations, we investigate the various factors that could explain the changes observed in poverty and inequality in the Philippines over the past decade. To do this, we decomposed per capita household income as a stochastic function of various forms of socio-economic capital and the socio-economic returns to capital. The results indicate that the higher levels of ownership of assets and higher economic returns to formal and non-agricultural employment have contributed to lower poverty while human capital and access to basic services remain stagnant and thus, had no impact on poverty and inequality. In general, we find that the impact of changes in socio-economic capital and changes in economic returns to capital as offsetting forces that contribute to slow poverty and inequality reduction despite the rapid economic growth that the Philippines has experienced over the past ten years.
Keywords: income decomposition, counterfactual simulation, poverty, inequality
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Year: 2016 Vol.: 65 No.: 1