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Geographically Weighted Multivariate Logistic Regression Model and Its Application

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  • M. Fathurahman
  • Purhadi
  • Sutikno
  • Vita Ratnasari

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.

Suggested Citation

  • M. Fathurahman & Purhadi & Sutikno & Vita Ratnasari, 2020. "Geographically Weighted Multivariate Logistic Regression Model and Its Application," Abstract and Applied Analysis, Hindawi, vol. 2020, pages 1-10, August.
    • Handle: RePEc:hin:jnlaaa:8353481
    DOI: 10.1155/2020/8353481
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    Cited by:

    1. Sarni Maniar Berliana & Purhadi & Sutikno & Santi Puteri Rahayu, 2020. "Parameter Estimation and Hypothesis Testing of Geographically Weighted Multivariate Generalized Poisson Regression," Mathematics, MDPI, vol. 8(9), pages 1-14, September.
    2. Jing Zhou & Bo Zhang & Yaowen Zhang & Yuhan Su & Jie Chen & Xiaofang Zhang, 2023. "Research on the Trade-Offs and Synergies of Ecosystem Services and Their Impact Factors in the Taohe River Basin," Sustainability, MDPI, vol. 15(12), pages 1-18, June.

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