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.
Highlights
Poisson distribution is one of the most widely utilized univariate and multivariate regression models for count data analysis
There are several multivariate count data models based on Poisson distribution, namely multivariate Poisson regression model, multivariate Poisson-gamma mixture model, multivariate Poisson log-normal model, and multivariate generalized Poisson regression model [1,2]
We developed a local regression model, called a geographically weighted multivariate generalized
Summary
Poisson distribution is one of the most widely utilized univariate and multivariate regression models for count data analysis. Bivariate and multivariate generalized Poisson regression models have been applied to real data with units of analysis such as elderly, women, patients, soccer teams, or households [2,5,6] These models are global regression models that assume the relationships between variables are spatially constant. The GWR model allows the relationships between response variables and explanatory variables vary over space so the regression coefficients systematically vary across space With this method, separate regression models are estimated for each location unit. Two GWR models for count data which use multivariate responses have been developed, based on the Poisson distribution and negative binomial distribution. We propose an alternative multivariate regression model that can handle any type of dispersion by taking into account spatial effects in measuring the relationships between the response variables and the explanatory variables.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
