Generalized Poisson Regression Modeling on the Number of Infant Deaths in East Nusa Tenggara Province in 2022

Robertus Guntur; Maria Ririanti  da Rato

doi:10.36456/jstat.vol17.no2.a9318

Authors

Robertus Guntur Universitas Nusa Cendana
Maria Ririanti da Rato Universitas Nusa Cendana

DOI:

https://doi.org/10.36456/jstat.vol17.no2.a9318

Keywords:

Number Of Infant Deaths, Generalized Poisson Regression (GPR), Overdispersion

Abstract

The number of infant deaths in East Nusa Tenggara (NTT) Province is still above the national average. This research was conducted to investigate the Generalized Poisson Regression (GPR) model for addressing the overdispersion in the Poisson regression model of the number of cases of infant deaths and to explore the potential factors influencing the number of infant deaths in the province. The variable used is the number infant mortality as a response variable, and the number of predictor variables that are thought to influence the response variable. The data used is secondary data obtained from the publication by the Central Statistics Agency of ENT Province from each of the 22 cities/regencies. The study shows data on the number of cases of infant mortality experienced overdispersion with a ratio between deviance and degrees of freedom of 3.578. Modeling with GPR shows that the model with 5 independent variables produces an optimal model with an AIC value of 184.145. Those variables are the percentage of households with access to adequate sanitation, the percentage of births assisted by parties other than medical personnel, the number of teenagers who received reproductive health counseling, the percentage of the population aged 0-59 months who received incomplete immunization, and the number of health facilities.

References

Nwanze LD, Siuliman A, Ibrahim N. Factors associated with infant mortality in Nigeria: A scoping review. PLoS ONE 2023;18(11):e0294434.

Databox. Provinsi dengan Angka Tertinggi di Indonesia. https://databoks.katadata.co.id/datapublish/2023/02/27/papua-provinsidengan-angka-kematian-bayi-tertinggi-di-indonesia.

Elisabeth Brielin Sinu, Ambrosius Dedi A. Sinu. Analisis Angka Kematian Bayi Di Provinsi Nusa Tenggara Timur Dengan Model Regresi Spasial. jkpu-nalanda 2023;1(6):287–299.

Maxwell O, Mayowa BA, Chinedu IU, Peace AE. Modelling Count Data; A Generalized Linear Model Framework. American Journal of Mathematics and Statistics 2018;8(6):179–183.

Chaniago AD, Wulandari SP. Pemodelan Generalized Poisson Regression (GPR) dan Negative Binomial Regression (NBR) untuk Mengatasi Overdispersi pada Jumlah Kematian Bayi di Kabupaten Probolinggo. JSSITS 2023;11(6):D448–D455.

Saputro DRS, Susanti A, Pratiwi NBI. The handling of overdispersion on Poisson regression model with the generalized Poisson regression model. Surakarta, Indonesia, 2021: 020026.

Guntur RD, Dappa JSB. PEMODELAN GENERALIZED POISSON REGRESSION PADA KASUS AIDS DI PROVINSI NUSA TENGGARA TIMUR TAHUN 2023. VARIANSI: Journal of Statistics and Its Application on Teaching and Research 2024;6(2):95–104.

Ibeji JU, Zewotir T, North D, Amusa L. Modelling fertility levels in Nigeria using Generalized Poisson regression-based approach. Scientific African 2020;9:e00494.

BPS NTT. Statistik Kesejahteraan Rakyat Provinsi Nusa Tenggara Timur 2022. Badan Pusat Statistik Provinsi NTT: Kupang, 2022 https://ntt.bps.go.id/id/publication/2022/12/28/6a49f879c5d9063b1c4b5396/statistik-kesejahteraan-rakyat-provinsi-nusa-tenggara-timur-2022.html.

Ihsan H, Sanusi W, Ulfadwiyanti R. Model Generalized Poisson Regression (GPR) dan Penerapannya pada Angka Pengangguran bagi Penduduk Usia Kerja di Provinsi Sulawesi Selatan. JMathCoS 2021;3(2):109.

Maneking FDG, Salaki DT, Hatidja D. Model Regresi Poisson Tergeneralisasi untuk Anak Gizi Buruk di Sulawesi Utara. JIS 2020;20(2):141.

Darnah. Mengatasi Overdispersi pada Model Regresi Poisson dengan Generalized Poisson Regresion I. Jurnal Eksponensial 2011;2(2):5–10.

Maulana AD, Anggraini D, Sukmawaty Y. PEMODELAN KEPADATAN PENDUDUK DENGAN PENDEKATAN REGRESI POISSON TERGENERALISASI. ragam 2023;2(1):33.

Dwi Putri A, Devianto D, Yanuar F. Pemodelan Jumlah Kematian Bayi di Kota Bandung dengan Menggunakan Regresi Zero-Inflated Poisson. Jurnal Matematika UNAND 2022;11(1):12.

R. K. Putri, M. Athoillah, and A. Haqiqiyah, “Analisis Faktor Yang Mempengaruhi Ketepatan Kelulusan Mahasiswa Dengan Algoritma Regresi Linear,” Jurnal Lebesgue: Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, vol. 5, no. 2, pp. 671–680, 2024.

W. Y. . Rusyida and A. Y. . Nugroho, “Multiperiod Logit on Survival Analysis of Financial Distress in Manufacturing Company”, J Statistika, vol. 16, no. 1, pp. 354–370, Jul. 2023.

Lais MF, Atti A, Pangaribuan RM, Guntur RD. Model Generalized Poisson Regression (GPR) Pada Kasus Stunting Di Provinsi Nusa Tenggara Timur. JD 2023;5(2):68–75.