Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression

被引：2

作者：

Chen, Zezhun ^{[1
]}

Dassios, Angelos ^{[1
]}

Tzougas, George ^{[2
,3
]}

机构：

[1] London Sch Econ, Dept Stat, Houghton St, London WC2A 2AE, England

[2] Heriot Watt Univ, Dept Actuarial Math & Stat, Edinburgh EH14 4AS, Midlothian, Scotland

[3] Heriot Watt Univ, Maxwell Inst Math Sci, Edinburgh EH14 4AS, Midlothian, Scotland

来源：

COMPUTATIONAL STATISTICS | 2023年 / 38卷 / 02期

关键词：

Count data time series; Multivariate INAR(1) regression models; Multivariate mixed Poisson-Generalized Inverse Gaussian; Correlated time series; Maximum likelihood estimation; TIME-SERIES; CLAIM COUNTS; MOTOR INSURANCE; LINEAR-MODELS; DISTRIBUTIONS;

D O I：

10.1007/s00180-022-01253-0

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：955 / 977

页数：23

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