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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/180409
Multivariate INAR(1) Regression Models Based on the Sarmanov Distribution.
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A multivariate INAR(1) regression model based on the Sarmanov distribution is proposed for modelling claim counts from an automobile insurance contract with different types of coverage. The correlation between claims from different coverage types is considered jointly with the serial correlation between the observations of the same policyholder observed over time. Several models based on the multivariate Sarmanov distribution are analyzed. The new models offer some advantages since they have all the advantages of the MINAR(1) regression model but allow for a more flexible dependence structure by using the Sarmanov distribution. Driven by a real panel data set, these models are considered and fitted to the data to discuss their goodness of fit and computational efficiency.
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BERMÚDEZ, Lluís and KARLIS, Dimitris. Multivariate INAR(1) Regression Models Based on the Sarmanov Distribution. Mathematics. 2021. Vol. 9, num. 505, pags. 1-13. ISSN 2227-7390. [consulted: 12 of June of 2026]. Available at: https://hdl.handle.net/2445/180409