Geographically and temporally weighted bivariate weibull regression model (studi kasus: kemiskinan dan angka harapan hidup di Provinsi Jawa Timur Tahun 2017-2021)

Prasetya, Muhammad Eka (2022) Geographically and temporally weighted bivariate weibull regression model (studi kasus: kemiskinan dan angka harapan hidup di Provinsi Jawa Timur Tahun 2017-2021). Masters thesis, Institut Teknologi Sepuluh Nopember.

Text 6003201027-Master_Thesis.pdf Restricted to Repository staff only Download (7MB) | Request a copy

Abstract

Salah satu distribusi teoritis variabel acak kontinu yang sering digunakan adalah distribusi Weibull. Apabila terdapat dua variabel respon yang saling berkorelasi maka metode yang tepat digunakan adalah Bivariate Weibull Regression (BWR). Dalam berbagai bidang penelitian banyak yang telah menggunakan data spasial. Model Geographically Weighted Bivariate Weibull Regression (GWBWR) menangani adanya efek spasial yang berupa heterogenitas spasial pada regresi bivariat dengan variabel respon berdistribusi Weibull. Dalam perkembangannya, banyak kasus yang membutuhkan informasi dari data panel. Penggunaan data panel dapat memberikan informasi yang lebih lengkap karena mencakup beberapa periode, tetapi memungkinkan adanya efek temporal. Penelitian ini mengembangkan model Geographically and Temporally Weighted Bivariate Weibull Regression (GTWBWR) untuk menangani heterogenitas spasial dan temporal secara bersamaan. Penaksiran parameter model menggunakan metode Maximum Likelihood Estimation (MLE) menunjukkan hasil yang tidak closed-form, sehingga dilanjutkan dengan iterasi numerik Berndt-Hall-Hall-Hausman (BHHH). Pengujian hipotesis serentak menggunakan metode Maximum Likelihood Ratio Test (MLRT). Selanjutnya model akan diaplikasikan pada pemodelan kemiskinan dan AHH di Jawa Timur pada tahun 2017-2021. Pemodelan menggunakan GTWBWR menghasilkan sebanyak 21 kelompok kabupaten/kota berdasarkan variabel signifikan terhadap respon. Dimana pada setiap kabupaten/kota memiliki variabel signifikan yang berbeda-beda, begitu pula antar periode di masing-masing kabupaten/kota. Hal ini menandakan bahwa signifikansi variabel prediktor terhadap respon kemiskinan dan AHH bervariasi dari sisi spasial maupun temporal.
====================================================================================================================================
One of the theoretical distributions of continuous random variables that is often used is the Weibull distribution. If there are two response variables that are correlated with each other, the appropriate method to use is Bivariate Weibull Regression (BWR). In various fields of research, many have used spatial data. The Geographically Weighted Bivariate Weibull Regression (GWBWR) model has a spatial effect in the form of spatial heterogeneity in bivariate regression with Weibull distributed response variables. In its development, many cases require information from the data panel. The use of panel data can provide more complete information because it covers several periods, but allows for a temporal effect. This study developed a Geographically and Temporally Weighted Bivariate Weibull Regression (GTWBWR) model to deal with spatial and temporal heterogeneity simultaneously. The parameter estimation model using the Maximum Likelihood Estimation (MLE) method shows results that are not closed, so it is continued with the Berndt-Hall-Hall-Hausman (BHHH) numerical iteration. Simultaneous hypothesis testing using the Maximum Likelihood Ratio Test (MLRT) method. Furthermore, the model will be applied to poverty and AHH modeling in East Java in 2017-2021. Modeling using GTWBWR resulted in 21 groups of districts/cities based on significant variables in response. Where each district/city has different significant variables, as well as between periods in each district/city. This indicates that the significance of the predictor variables on the response to poverty and AHH varies from a spatial and temporal perspective.

Item Type:	Thesis (Masters)
Additional Information:	RTSt 519.536 Pra g-1 2022
Uncontrolled Keywords:	Kemiskinan, Angka Harapan Hidup, GTWBWR, MLE, MLRT, Poverty, Life Expectancy Rate, GTWBWR, MLE, MLRT
Subjects:	Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49101-(S2) Master Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	29 Apr 2026 09:03
Last Modified:	29 Apr 2026 09:03
URI:	http://repository.its.ac.id/id/eprint/132940

Actions (login required)

View Item