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Record ID: 89 [ Page 8 of 16, No. 1 ]
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 8 of 16, No. 2 ]
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 8 of 16, No. 3 ]
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 8 of 16, No. 4 ]
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 8 of 16, No. 5 ]
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 8 of 16, No. 6 ]
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 8 of 16, No. 7 ]
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 8 of 16, No. 8 ]
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 8 of 16, No. 9 ]
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 8 of 16, No. 10 ]
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