Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

Zezhun Chen; Angelos Dassios; George Tzougas

doi:10.1007/s00180-022-01253-0

Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

Zezhun Chen^*, Angelos Dassios, George Tzougas

^*Corresponding author for this work

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

3 Citations (Scopus)

23 Downloads (Pure)

Abstract

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.

Original language	English
Pages (from-to)	955-977
Number of pages	23
Journal	Computational Statistics
Volume	38
Issue number	2
Early online date	9 Jul 2022
DOIs	https://doi.org/10.1007/s00180-022-01253-0
Publication status	Published - Jun 2023

Keywords

Correlated time series
Count data time series
Maximum likelihood estimation
Multivariate INAR(1) regression models
Multivariate mixed Poisson-Generalized Inverse Gaussian

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Computational Mathematics

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title = "Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression",

abstract = "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.",

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author = "Zezhun Chen and Angelos Dassios and George Tzougas",

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AU - Chen, Zezhun

AU - Dassios, Angelos

AU - Tzougas, George

N1 - Funding Information: The authors would like to thank the anonymous referees for their very helpful comments and suggestions which have significantly improved this article. Publisher Copyright: © 2022, The Author(s).

PY - 2023/6

Y1 - 2023/6

N2 - 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.

AB - 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.

KW - Correlated time series

KW - Count data time series

KW - Maximum likelihood estimation

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ER -

Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

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