Parameter Estimation and Hypothesis Testing of Geographically Weighted Multivariate Generalized Poisson Regression

Sarni Maniar Berliana, Purhadi, Sutikno and Santi Puteri Rahayu
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Sarni Maniar Berliana: Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Purhadi: Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Sutikno: Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Santi Puteri Rahayu: Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia

Mathematics, 2020, vol. 8, issue 9, 1-14

Abstract: We introduce a new multivariate regression model based on the generalized Poisson distribution, which we called geographically-weighted multivariate generalized Poisson regression (GWMGPR) model, and we present a maximum likelihood step-by-step procedure to obtain parameters for it. We use the maximum likelihood ratio test to examine the significance of the regression parameters and to define their critical region.

Keywords: spatial analysis; distribution theory; maximum likelihood estimation; likelihood ratio test (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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