Georaphically Weighted Ridge Regression Modelling on 2023 Poverty Indicators Data in the Provinces of West Kalimantan and Central Kalimantan
Article Sidebar
Main Article Content
Abstract
Regression analysis is a method to explain the relations between independent variables and a dependent variable. Linear regression analysis relies on certain assumptions, one of the assumption is homogeneity. However, there is a situation when the variance at each observation differs or called spatial heterogeneity.This issue can be solved using Geographically Weighted Regression (GWR), a statistical method that can be fixed spatial heterogeneity by adding a local weighted matrix, the result in GWR model is a local model for each observation point. However, GWR has a limitation, it cannot handle multicollinearity. Ridge regression is a method used to solved multicollinearity by adding a bias constant (λ). A GWR model that contains multicollinearity and fixed using ridge regression is known as Geographically Weighted Ridge Regression (GWRR).
Article Details
References
[1] A. Fotheringham, C. Brunsdon, and M. Charlton. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley and Sons, Ltd., England, 2002.
[2] E. M. Mujiati, Yundari, and N. M. Huda. Pemodelan geographically weighted regression pada angka partisipasi ©2024 The Authors.
[3] M. R. Ikhsanudin and E. Pasaribu. Modeling the percentage of poor population in Java Island using geographically weighted regression approach. Jurnal Matematika, Statistika, dan Komputasi, 20(1):224–229, 2023.
[4] A. N. Septiyana, I. Fatkhurrohman, F. F. Fikri, R. S, A. T. Prananggalih, A. B. Bahtiar, D. S. ML, and S. M. Berliana. Pemodelan geographically weighted regression pada tingkat pengangguran terbuka di Pulau Jawa tahun 2020. In Seminar Nasional Official Statistics, pages 345–737, 2023.
[5] R. Erdkhadifa. Pemodelan spasial tingkat pengangguran terbuka di Jawa Timur dengan geographically weighted regression. Jurnal Statistika, 21(2):85–97, 2021.
[6] P. F. Utami, A. Rusgiyono, and D. Ispriyanti. Pemodelan semiparametric geographically weighted regression pada kasus pneumonia balita Provinsi Jawa Tengah. Jurnal Gaussian, 10(2):250–258, 2021.
[7] Y. Farida, M. R. Nurfadila, P. K. Intan, H. Khaulasari, N. Ulinnuha, W. D. Utami, and D. Yuliati. Modeling the flood disaster in South Kalimantan using geographically weighted regression and mixed geographically weighted regression. ITM Web of Conferences, 58:1–11, 2024.
[8] W. Nuryati and Suliadi. Pengujian pada regresi ridge dan penerapannya terhadap data produk domestik regional bruto Provinsi Jawa Barat. Jurnal Statistics, 3(2):486–492, 2023.
[9] F. Fatmawati and R. Y. Suratman. Performa regresi ridge dan regresi lasso pada data dengan multikolinearitas. Leibniz: Jurnal Matematika, 2(2):1–10, 2022.
[10] A. H. Arrasyid, D. Ispriyanti, and A. Hoyyi. Metode modified jackknife ridge regression dalam penanganan multikolinieritas. Jurnal Gaussian, 10(1):104–113, 2021.
[11] N. Delvia, Mustafid, and H. Yasin. Geographically weighted negative binomial regression untuk menangani overdispersi pada jumlah penduduk miskin. Jurnal Gaussian, 10(4):532–543, 2021.
[12] F. Cholid. Perbandingan geographically weighted regression dengan mixed geographically weighted regression. Jurnal Statistika, 23(2):96–109, 2023.
[13] A. Sharma. Exploratory spatial analysis of food insecurity and diabetes: An application of multiscale geographically weighted regression. Annals of GIS, 29(4):485–498, 2023.
[14] L. Laome, I. N. Budiantara, and V. Ratnasari. Estimation curve of mixed spline truncated and Fourier series estimator for geographically weighted nonparametric regression. Mathematics, 11:1–13, 2023.
[15] A. Fadliana, H. Pramoedyo, and R. Fitriani. Implementation of locally compensated ridge geographically weighted regression model in spatial data with multicollinearity problems. Media Statistika, 13(2):125–135, 2020.
[16] F. Andrian and A. S. Yundari. Pemodelan geographically weighted ridge regression pada tingkat pengangguran terbuka di Kalimantan Barat. J. Diferensial, 5(2):83–95, 2023.
[17] A. Y. Qur’ani, M. A. D. Octavanny, and R. S. Widiastuti. Estimasi parameter model geographically weighted ridge regression pada indikator pengukuran penanganan stunting di Indonesia. Oktal: Jurnal Ilmu Komputer dan Science, 2(8):2245–2253, 2023.
[18] N. R. Draper and H. Smith. Applied Regression Analysis: Third Edition. John Wiley & Sons, New York, 1998.
[19] Y. Leung, C.-L. Mei, and W. Zhang. Statistical test for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A, 32:9–32, 2000.
[20] D. C. Wheeler. Diagnostic tools and a remedial method for collinearity in geographically weighted regression. Environment and Planning A, 39:2464–2481, 2007.