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EM estimation for bivariate mixed poisson INAR(1) claim count regression models with correlated random effects

Chen, Zezhun Chen, Dassios, Angelos ORCID: 0000-0002-3968-2366 and Tzougas, George (2023) EM estimation for bivariate mixed poisson INAR(1) claim count regression models with correlated random effects. European Actuarial Journal. ISSN 2190-9733

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Identification Number: 10.1007/s13385-023-00351-7

Abstract

This article considers bivariate mixed Poisson INAR(1) regression models with correlated random effects for modelling correlations of different signs and magnitude among time series of different types of claim counts. This is the first time that the proposed family of INAR(1) models is used in a statistical or actuarial context. For expository purposes, the bivariate mixed Poisson INAR(1) claim count regression models with correlated Lognormal and Gamma random effects paired via a Gaussian copula are presented as competitive alternatives to the classical bivariate Negative Binomial INAR(1) claim count regression model which only allows for positive dependence between the time series of claim count responses. Our main achievement is that we develop novel alternative Expectation-Maximization type algorithms for maximum likelihood estimation of the parameters of the models which are demonstrated to perform satisfactory when the models are fitted to Local Government Property Insurance Fund data from the state of Wisconsin.

Item Type: Article
Official URL: https://www.springer.com/journal/13385
Additional Information: © 2023 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 10 May 2023 09:30
Last Modified: 09 Jun 2024 16:24
URI: http://eprints.lse.ac.uk/id/eprint/118826

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