Mixed Geographically Weighted Poisson Regression Model in The Number of Maternal Mortality
Main Article Content
Abstract
This study aims to prove and to use the multiple regression analysis methods can be developed into Poisson regression because the total data follows the assumption of a Poisson distribution. Conditions that occur in poisson regression obtained a global regression coefficient value, which means that each observation point has generalized observation characteristics influenced by the same variables. Based on this problem, a spatial regression is developed where the geographical weighting that called geographically weighted Poisson regression. The results obtained in the geographically weighted poisson regression method allow for variables that have an effect at all observation points. So developed by geographically weighted Poisson regression semiparametric method. This method is implemented in solving the problem of maternal mortality in East Java which is thought to be influenced by the some independent variables. In this study, using East Java data in 2019. The results of mixed geographically weighted poisson regression results, with kernel function used in the analysis is fixed gaussian and the variable number of health facilities was used as a global variable, and obtained 12 groups with local variables that both had a significant effect
Downloads
Article Details
Arkandi, I., & Winahju, W. S. (2015). Analisis Faktor Risiko Kematian Ibu dan Kematian Bayi dengan Pendekatan Regresi Poisson Bivariat di Provinsi Jawa Timur Tahun 2013. Jurnal Sains Dan Seni ITS, 4(2), 139–144.
Darnah, D. (2010). Menentukan Model Terbaik dalam Regresi Poisson dengan Menggunakan Koefisien Determinasi. JMSK: Jurnal Matematika, Statistika, Dan Komputasi, 6(2), 59–71.
Destyanugraha, R., & Kurniawan, R. (2017). Pemodelan Angka Kematian Ibu Di Indonesia Dengan Pendekatan Geographically Weighted Poisson Regression. Jurnal Matematika Sains Dan Teknologi (JMST), 18(2), 76–94. https://doi.org/https://doi.org/10.33830/jmst.v18i2.131.2017
Dong, G., Nakaya, T., & Brunsdon, C. (2018). Geographically weighted regression models for ordinal categorical response variables: An application to geo-referenced life satisfaction data. Computers, Environment and Urban Systems, 70, 35–42. https://doi.org/10.1016/j.compenvurbsys.2018.01.012
Khaulasari, H. (2019). Model Geographically Weighted Poisson Regression Pada Jumlah Kasus Penderita Tuberculosis di Surabaya. Prosiding Seminar Nasional Integrasi Matematika Dan Nilai Islami, 3(1), 1–10.
Khaulasari, H., & Antonius, R. (2019). Model Mixed Geographically Weighted Poisson Regression Dengan Pembobot Fungsi Kernel Fixed Bi-Square Pada Penderita Tuberculosis Di Surabaya. Statistika, 7(2), 71–83. https://doi.org/https://doi.org/10.26714/jsunimus.7.2.2019.%25p
Khoeriyah, R. Y., & Hajarisman, N. (2021). Regresi Terboboti Geografis Semiparametrik (RTG-S) untuk Pemodelan Indeks Pembangunan Kesehatan Masyarakat Kabupaten/Kota di Sumatera Utara. Jurnal Riset Statistika, 1(1), 43–50. https://doi.org/10.29313/jrs.v1i1.145
Li, Z., Wang, W., Liu, P., Bigham, J. M., & Ragland, D. R. (2013). Using Geographically Weighted Poisson Regression for county-level crash modeling in California. Safety Science, 58, 89–97. https://doi.org/10.1016/j.ssci.2013.04.005
Lu, B., Harris, P., Charlton, M., & Brunsdon, C. (2015). Calibrating a Geographically Weighted Regression Model with Parameter-specific Distance Metrics. Procedia Environmental Sciences, 26, 109–114. https://doi.org/10.1016/j.proenv.2015.05.011
Maneking, F. D. G., Salaki, D. T., & Hatidja, D. (2020). Model Regresi Poisson Tergeneralisasi untuk Anak Gizi Buruk di Sulawesi Utara. Jurnal Ilmiah Sains, 20(2), 141. https://doi.org/10.35799/jis.20.2.2020.29133
Octavianty, O., Toharudin, T., & Jaya, I. G. N. M. (2017). Geographically weighted poisson regression semiparametric on modeling of the number of tuberculosis cases (Case study: Bandung city). AIP Conference Proceedings, 1827, 1–6. https://doi.org/10.1063/1.4979438
Saefuddin, A., Saepudin, D., & Kusumaningrum, D. (2013). Geographically Weighted Poisson Regression(GWPR) for Analyzing The Malnutrition Data in Java-Indonesia. 53rd Congress of the European Regional Science Association: “Regional Integration: Europe, the Mediterranean and the World Economy,” 1–19. http://hdl.handle.net/10419/124131
Sundari, I. (2012). Regresi Poisson dan Penerapannya Untuk Memodelkan Hubungan Usia dan Perilaku Merokok Terhadap Jumlah Kematian Penderita Penyakit Kanker Paru-Paru. Jurnal Matematika UNAND, 1(1), 71–76. https://doi.org/https://doi.org/10.25077/jmu.1.1.71-76.2012
Utami, P. F., Rusgiyono, A., & Ispriyanti, D. (2021). Pemodelan Semiparametric Geographically Weighted Regression Pada Kasus Pneumonia Balita Provinsi Jawa Tengah. Jurnal Gaussian, 10(2), 250–258. https://ejournal3.undip.ac.id/index.php/gaussian/
Yasin, H. (2013). Uji Hipotesis Model Mixed Geographically Weighted Regression dengan Metode Bootstrap. 10th SNS Seminar 2013, 527–536.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.