2020 Geographically Weighted Multivariate Logistic Regression Model and Its Application
M. Fathurahman, Purhadi, Sutikno, Vita Ratnasari
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Abstr. Appl. Anal. 2020: 1-10 (2020). DOI: 10.1155/2020/8353481

Abstract

This study investigates the geographically weighted multivariate logistic regression (GWMLR) model, parameter estimation, and hypothesis testing procedures. The GWMLR model is an extension to the multivariate logistic regression (MLR) model, which has dependent variables that follow a multinomial distribution along with parameters associated with the spatial weighting at each location in the study area. The parameter estimation was done using the maximum likelihood estimation and Newton-Raphson methods, and the maximum likelihood ratio test was used for hypothesis testing of the parameters. The performance of the GWMLR model was evaluated using a real dataset and it was found to perform better than the MLR model.

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M. Fathurahman. Purhadi. Sutikno. Vita Ratnasari. "Geographically Weighted Multivariate Logistic Regression Model and Its Application." Abstr. Appl. Anal. 2020 1 - 10, 2020. https://doi.org/10.1155/2020/8353481

Information

Received: 30 March 2020; Revised: 22 May 2020; Accepted: 15 June 2020; Published: 2020
First available in Project Euclid: 28 July 2020

Digital Object Identifier: 10.1155/2020/8353481

Rights: Copyright © 2020 Hindawi

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