Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models

Amalia, Junita; Purhadi, Purhadi; Otok, Bambang Widjanarko

doi:10.1063/1.5012224

Cited by 5 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Globally, the bivariate Poisson inverse Gaussian regression (BPIGR) model was developed by [9], while the multivariate Poisson inverse Gaussian regression (MPIGR) was developed by [10,18]. Locally, the Geographically Weighted Poisson Inverse Gaussian Regression (GWPIGR) model was created by [19]. For bivariate cases, Ref.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Geographically Weighted Multivariate Poisson Inverse Gaussian Regression Model and Its Applications

et al. 2022

View full text Add to dashboard Cite

This study aims to develop a method for multivariate spatial overdispersion count data with mixed Poisson distribution, namely the Geographically Weighted Multivariate Poisson Inverse Gaussian Regression (GWMPIGR) model. The parameters of the GWMPIGR model are estimated locally using the maximum likelihood estimation (MLE) method by considering spatial effects. Therefore, the significance of the regression parameter differs for each location. In this study, four GWMPIGR models are evaluated based on the exposure variable and the spatial weighting function. We compare the performance of those four models in real-world application using data on the number of infant, under-5 and maternal deaths in East Java in 2019 using five predictor variables. In this study, the GWMPIGR model uses one exposure variable and three exposure variables. Compared to the fixed kernel Gaussian weighting function, the GWMPIGR model with the fixed kernel bisquare weighting function and one exposure variable has a better fit based on the AICc value. Furthermore, according to the best GWMPIGR model, there are several regional groups formed based on predictors that significantly affected each event in East Java in 2019.

show abstract

Section: Introductionmentioning

confidence: 99%

“…For bivariate cases, Ref. [20] developed the Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model. To accommodate a combination of global and local parameter estimates, Ref.…”

Section: Introductionmentioning

confidence: 99%

The Geographically Weighted Multivariate Poisson Inverse Gaussian Regression Model and Its Applications

et al. 2022

View full text Add to dashboard Cite

show abstract

Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

2022

View full text Add to dashboard Cite

In this paper, we present a novel family of multivariate mixed Poisson-Generalized Inverse Gaussian INAR(1), MMPGIG-INAR(1), regression models for modelling time series of overdispersed count response variables in a versatile manner. The statistical properties associated with the proposed family of models are discussed and we derive the joint distribution of innovations across all the sequences. Finally, for illustrative purposes different members of the MMPGIG-INAR(1) class are fitted to Local Government Property Insurance Fund data from the state of Wisconsin via maximum likelihood estimation.

show abstract

Application of Geographically Weighted Bivariate Poisson Inverse Gaussian Regression

Amalia

Purhadi²,

Otok³

2020

The 4th Indoms International Conference on Mathematics and Its Application (Iicma 2019)

View full text Add to dashboard Cite

Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models

Cited by 5 publications

References 8 publications

The Geographically Weighted Multivariate Poisson Inverse Gaussian Regression Model and Its Applications

The Geographically Weighted Multivariate Poisson Inverse Gaussian Regression Model and Its Applications

Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

Application of Geographically Weighted Bivariate Poisson Inverse Gaussian Regression

Contact Info

Product

Resources

About