EM estimation for bivariate mixed poisson INAR(1) claim count regression models with correlated random effects
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20240013622 | ||
| 003 | MAP | ||
| 005 | 20240902115320.0 | ||
| 008 | 240830e20240415che|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | 1 | $0MAPA20240021139$aChen, Zezhun | |
| 245 | 1 | 0 | $aEM estimation for bivariate mixed poisson INAR(1) claim count regression models with correlated random effects$cZezhun Chen, Angelos Dassios and George Tzougas |
| 520 | $aThis 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 | ||
| 650 | 4 | $0MAPA20080602437$aMatemática del seguro | |
| 650 | 4 | $0MAPA20080556495$aSiniestros | |
| 650 | 4 | $0MAPA20080545338$aSeguros | |
| 650 | 4 | $0MAPA20080586294$aMercado de seguros | |
| 700 | 1 | $0MAPA20120016989$aDassios, A. | |
| 700 | 1 | $0MAPA20140009800$aTzougas, George | |
| 773 | 0 | $wMAP20220007085$g15/04/2024 Volúmen 14 - Número 1 - abril 2024 , p.225-255$tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 | |
| 856 | $uhttps://link.springer.com/article/10.1007/s13385-023-00351-7 |