A multi-predictor nonparametric local linear generalized Poisson regression model for overdispersed count data.
11 affiliations
- 1Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia.
- 2Nutrition Study Program, Faculty of Public Health, Mulawarman University, Samarinda, Indonesia.
- 3Statistics Study Program, Department of Mathematics, Faculty of Sciences and Technology, Airlangga University, Surabaya, Indonesia.
- 4School of Industrial Technology, Universiti Sains Malaysia, 11800 Penang, Malaysia.
- 5Polytechnic of Statistics STIS, Jakarta, Indonesia.
- 6Biology Study Program, Department of Biology, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia.
- 7Physics Study Program, Department of Physics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia.
- 8Physics Study Program, Faculty of Mathematics and Natural Sciences, Palangka Raya University, Palangka Raya, Indonesia.
- 9Mathematics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia.
- 10Universitas Muhammadiyah Kalimantan Timur, Samarinda, Indonesia.
- 11Environmental Science Study Program, Department of Biology, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia.
Abstract
Generalized Poisson regression is widely used for modeling overdispersed count data; however, existing nonparametric local linear generalized Poisson regression methods are generally restricted to a single predictor, limiting their applicability to datasets involving multiple predictors. This study extends the nonparametric local linear generalized Poisson regression framework to accommodate multiple predictors, namely multi-predictor local linear generalized Poisson regression (MP-LLGPR). The proposed method provides a flexible approach for modeling overdispersed count response variable with locally varying relationships involving multiple predictors. The method is empirically validated in a healthcare application using stunting count data from 44 regencies and cities in Kalimantan, Indonesia. The empirical validation demonstrates the applicability of the proposed method for modeling overdispersed count data with multiple predictors and provides a comparison with the corresponding global generalized Poisson regression model.
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BibTeX
@article{darnah2026multipredictor,
title = {{A multi-predictor nonparametric local linear generalized Poisson regression model for overdispersed count data.}},
author = {Darnah and M Fathurahman and Iriyani Kamaruddin and Suyitno and Toha Saifudin and Mardiana Idayu Ahmad and Erni Tri Astuti and Ratna Kusuma and Rahmawati Munir and Made Dirgantara and Fidia Deny Tisna Amijaya and Imam Rofi'i and Andi Syaifullah Al Maslulil Agri and Feni Arista Bahni},
journal = {MethodsX},
year = {2026},
month = oct,
doi = {10.1016/j.mex.2026.104228},
url = {https://europepmc.org/article/MED/42999864},
note = {CC BY},
}
RIS
TY - JOUR
TI - A multi-predictor nonparametric local linear generalized Poisson regression model for overdispersed count data.
AU - Darnah
AU - M Fathurahman
AU - Iriyani Kamaruddin
AU - Suyitno
AU - Toha Saifudin
AU - Mardiana Idayu Ahmad
AU - Erni Tri Astuti
AU - Ratna Kusuma
AU - Rahmawati Munir
AU - Made Dirgantara
AU - Fidia Deny Tisna Amijaya
AU - Imam Rofi'i
AU - Andi Syaifullah Al Maslulil Agri
AU - Feni Arista Bahni
PY - 2026
DA - 2026/10/23
JO - MethodsX
DO - 10.1016/j.mex.2026.104228
UR - https://europepmc.org/article/MED/42999864
AB - Generalized Poisson regression is widely used for modeling overdispersed count data; however, existing nonparametric local linear generalized Poisson regression methods are generally restricted to a single predictor, limiting their applicability to datasets involving multiple predictors. This study extends the nonparametric local linear generalized Poisson regression framework to accommodate multiple predictors, namely multi-predictor local linear generalized Poisson regression (MP-LLGPR). The proposed method provides a flexible approach for modeling overdispersed count response variable with locally varying relationships involving multiple predictors. The method is empirically validated in a healthcare application using stunting count data from 44 regencies and cities in Kalimantan, Indonesia. The empirical validation demonstrates the applicability of the proposed method for modeling overdispersed count data with multiple predictors and provides a comparison with the corresponding global generalized Poisson regression model.
SN - 2215-0161
ER -