Parameter estimation and hypothesis testing of geographically weighted multivariate generalized poisson regression

Sarni Maniar Berliana; Santi Puteri Rahayu

doi:10.3390/math8091523

Parameter estimation and hypothesis testing of geographically weighted multivariate generalized poisson regression

Sarni Maniar Berliana, Purhadi^*, Sutikno, Santi Puteri Rahayu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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.

Original language	English
Article number	1523
Journal	Mathematics
Volume	8
Issue number	9
DOIs	https://doi.org/10.3390/math8091523
Publication status	Published - Sept 2020

Keywords

Distribution theory
Likelihood ratio test
Maximum likelihood estimation
Spatial analysis

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10.3390/math8091523

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title = "Parameter estimation and hypothesis testing of geographically weighted multivariate generalized poisson regression",

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 = "Distribution theory, Likelihood ratio test, Maximum likelihood estimation, Spatial analysis",

author = "Berliana, {Sarni Maniar} and Purhadi and Sutikno and Rahayu, {Santi Puteri}",

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AU - Berliana, Sarni Maniar

AU - Purhadi,

AU - Sutikno,

AU - Rahayu, Santi Puteri

PY - 2020/9

Y1 - 2020/9

N2 - 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.

AB - 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.

KW - Distribution theory

KW - Likelihood ratio test

KW - Maximum likelihood estimation

KW - Spatial analysis

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DO - 10.3390/math8091523

M3 - Article

AN - SCOPUS:85091370728

SN - 2227-7390

VL - 8

JO - Mathematics

JF - Mathematics

IS - 9

M1 - 1523

ER -

Parameter estimation and hypothesis testing of geographically weighted multivariate generalized poisson regression

Abstract

Keywords

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