ANALISIS PENGARUH ANGKA KEMATIAN BAYI TERHADAP ANGKA HARAPAN HIDUP DI PROVINSI JAWA TIMUR BERDASARKAN ESTIMATOR LEAST SQUARE SPINE
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Life expectancy can be used to evaluate the government's performance for improving the welfare of the population in the health sector. Life expectancy is closely related to infant mortality rate. Theoretically, decreasing of infant mortality rate will cause increasing of life expectancy. A statistical method that can be used to model life expectancy is nonparametric regression model based on least square spline estimator. This method provides high flexibility to accommodate pattern of data by using smoothing technique. The best estimated model is order one spline model with one knot based on minimum generalized cross validation (GCV) value of 0.607. Each increasing of one infant mortality rate unit will cause decreasing of life expectancy of 0.2314 for infant mortality rate less than 27, and of 0.0666 for infant mortality rate more than and equals to 27. In addition, based on mean square error (MSE) of 0.492 and R2value of 76.59% for nonparametric model approach compared with MSE of 0.634 and R2 value of 71.8% for parametric model approach, we conclude that the use of nonparametric model approach based on least square spline estimator is better than that of parametric model approach.
Pramono, M.S., Suci, Wulansari., dan Sutikno., 2012, Pemetaan Determinan Angka Kematian Bayi di Jawa Timur Berdasarkan Indikator Indeks Pembangunan Kesehatan Masyarakat., Bulletin of Health System Research, 15: 38-46.Juliandari, N.Y.N., dan Budiantara, I.N., 2014, Pemodelan Angka Harapan Hidup dan Angka Kematian Bayi di Jawa Timur dengan Pendekatan Regresi Nonparametrik Spline Birespon, ITS Paper, Surabaya.
Chamidah, N., Kurniawan, A., Zaman, B., Muniroh, L., 2018, Least square-spline estimator in Multiresponse semiparametric regression model for estimating median growth charts of children in East Java Indonesia, Far East Journal of Mathematical Sciences (FJMS), 107 (2),295-307.
Lestari, B., Budiantara, I.N., Sunaryo, S., and Mashuri, M., 2012, Spline smoothing for multi-response nonparametric regression model in case of heteroscedasticity of variance. Journal of Mathemathics and Statistics, 8 (3), 337-384.
Islamiyati A., Fatmawati and Chamidah N., 2018, Estimation of Covariance Matrix on Bi-Response Longitudinal Data Analysis with Penalized Spline Regression. Journal of Physics: Conf. Series, 979 012093, IOP Publishing. doi :10.1088/1742-6596/979/1/012093.
Aydin, D. and Yilmaz, E., 2018, Modified spline regression based on randomly right-censored data: A comparative study, Communications in Statistics-Simulation and Computation, Volume 47, Issue 9.
Chamidah, N., and Lestari, B., 2016. Spline estimator in homoscedastic multi-response nonparametric regression model in case of unbalanced number of observations, Far East Journal of Mathematical Sciences (FJMS), 100 (9), 1433-1453.