Pemodelan Regresi Nonparametrik Spline Poisson Pada Tingkat Kematian Bayi di Sulawesi Selatan
Keywords:
GCV, Multivariable Nonparametric Regression, Poisson, Spline Truncated, Total Infant MortalityAbstract
Poisson regression analysis is a method used to analyze the relationship between predictor variables and response variables with a Poisson distribution. However, not all data have an orderly pattern, so the Poisson regression is not appropriate to use. To solve this problem, a multivariable Poisson nonparametric regression with a spline truncated estimator was used. In this research, the estimation parameters of multivariable Poisson nonparametric regression was applied to data of infant mortality rates in South Sulawesi in 2017. The infant mortality rate can be measured from the number of infant deaths under one year. The method of selecting the optimal knot point uses the Generalized Cross Validation (GCV) method. The best model is formed on a linear spline model with one knot point. Based on the estimation of the parameters formed, it shows that the variable number of babies with low birth weight (x1) and the number of infants who are exclusively breastfed (x3) significantly affect the number of infant deaths.
Keywords: GCV, Multivariable Nonparametric Regression, Poisson, Spline Truncated, Total Infant Mortality.
References
Jus’at, I. Analisa Regresi Pengolahan Data Gizi & Kesehatan. Yogyakarta: Rapha Publishing. 2019.
Dinas Kesehatan Sulawesi Selatan. Profil Kesehatan Provinsi Sulawesi Selatan Tahun 2016. Makassar: Pusat Data dan Informasi. 2017.
Aulele, S. N. Pemodelan Jumlah Kematian Bayi di Provinsi Maluku Tahun 2010 Dengan Menggunakan Regresi Poisson. Jurnal Barekeng, 5(2): 23–27, 2012.
Koerniawan, V., Sunusi, N. dan Raupong. Estimasi Parameter Model Poisson Hidden Markov pada Data Banyaknya Kedatangan Klaim Asuransi Jiwa. Estimasi: Journal of Statistics and Its Application, 1(2): 65-73, 2020.
Safrida, N., dkk. Aplikasi Model Regresi Poisson Tergeneralisasi Pada Kasus Angka Kematian Bayi di Jawa Tengah Tahun 2007. JURNAL GAUSSIAN, 2(4): 361–368, 2013.
Prahutama, A., dkk. Analisis Faktor-Faktor yang Mempengaruhi Angka Kematian Bayi di Jawa Tengah Menggunakan Regresi Generalized Poisson dan Binomial Negatif. Jurnal Statistika, 5(2), 2017.
Ramdhani, Z. A., Islamiyati, A. dan Raupong. Hubungan Faktor Kolesterol Terhadap Gula Darah Diabetes dengan Spline Kubik Terbobot. Estimasi: Journal of Statistics and Its Application, 1(1): 32-39, 2020.
Arifin, S., Islamiyati, A. dan Raupong. Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama. Estimasi: Journal of Statistics and Its Application, 1(1): 40-47, 2020.
Pratiwi, L.P.S. Pemodelan Spline Truncated Dalam Regresi Nonparametrik Birespon. Konferensi Nasonal Sistem & Informatika, 2017.
Alexander, M. dan Alkema, L. Global Estimation of Neonatal Mortality Using a Bayesian Hierarchical Splines Regression Model. Demographic Research, 38: 335–372, 2018.
Kilinc, B. K. dan Asfha, H. D. Penalized Splines Fitting for a Poisson Response Including Outliers. Pakistan Journal of Statistics and Operation Research, 15(15): 979–988, 2019.
Collett, D. Modelling Binary Data, Second Edition. United States: CRC Press. 2002.
Hardle, W. Apllied Nonparametric Regression. New York: Cambridge University Press. 1990.
Agresti, A. An Introduction To Categorical Data Analysis Second Edition. New Jersey: John Wiley & Sons. 2007.
Budiantara, I. N.. Model Spline dengan Knots Optimal. Jurnal Ilmu Dasar, 7: 77– 85, 2006.
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