The Philippine Statistician

The Philippine Statistician (TPS) is a refereed scientific journal published twice a year. TPS aims to disseminate a wide range of papers of technical, theoretical, and applied statistical nature considered of general or special interest to varied groups of statisticians.

The Commission of Higher Education (CHED) recognized The Philippine Statistician as one of CHED Accredited Research Journals.

Printer friendly version

No. of records per page: 10 | 20 | 30 | 50 | 100 | Show all
Select a Page:  << Previous 1 2 3 4 5 6 7 8 9 10 11 Next >>
No. Title of Article Authors Year Vol No PDF
1.Comparison of Regression Estimator and Ratio Estimator: A Simulation StudyDixi M. Paglinawan2017661
2.A Class of Ratio-Cum-Product Type Exponential Estimators under Simple Random SamplingGajendra K. Vishwakarma and Sayed Mohammed Zeeshan2017661
3.An Exponentially Weighted Moving Average Control Chart for Zero-Truncated Poisson Processes: A Design and Analytic Framework with Fast Initial Response FeatureRobert Neil F. Leong, Frumencio F. Co, Vio Jianu C. Mojica and Daniel Stanley Y. Tan2017661
4.Spatial-Temporal Models and Computational Statistics Methods: A SurveyErniel B. Barrios and Kevin Carl P. Santos2017661
5.A Sustainability Model for Small Health Maintenance ProgramsMia Pang Rey and Ivy D.C. Suan2016652
6.Multiple Statistical Tools for Divergence Analysis of Rice (Oryza sativa L.) Released VarietiesAldrin Y. Cantila, Sailila E. Abdula, Haziel Jane C. Candalia and Gina D. Balleras2016652
7.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 FruitDivo Dharma Silalahi, Consorcia E. ReaƱo, Felino P. Lansigan, Rolando G. Panopio and Nathaniel C. Bantayan2016652
8.Quantile and Restricted Maximum Likelihood Approach for Robust Regression of Clustered DataMay Ann S. Estoy and Joseph Ryan G. Lansangan2016652
9.Nonparametric Hypothesis Testing for Isotonic Survival Models with ClusteringJohn D. Eustaquio2016652
10.Semiparametric Probit Model for High-dimensional Clustered DataDaniel R. Raguindin and Joseph Ryan G. Lansangan2016652

Back to top