On the Type I multivariate zero-truncated hurdle model with applications in health insurance

Pengcheng Zhang, Enrique Calderin, Shuanming Li and Xueyuan Wu

Insurance: Mathematics and Economics, 2020, vol. 90, issue C, 35-45

Abstract: In the general insurance modeling literature, there has been a lot of work based on univariate zero-truncated models, but little has been done in the multivariate zero-truncation cases, for instance a line of insurance business with various classes of policies. There are three types of zero-truncation in the multivariate setting: only records with all zeros are missing, zero counts for one or some classes are missing, or zeros are completely missing for all classes. In this paper, we focus on the first case, the so-called Type I zero-truncation, and a new multivariate zero-truncated hurdle model is developed to study it. The key idea of developing such a model is to identify a stochastic representation for the underlying random variables, which enables us to use the EM algorithm to simplify the estimation procedure. This model is used to analyze a health insurance claims dataset that contains claim counts from different categories of claims without common zero observations.

Keywords: Type I multivariate zero-truncation; Hurdle model; EM algorithm; Health insurance modeling; Claim frequency modeling (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:90:y:2020:i:c:p:35-45

DOI: 10.1016/j.insmatheco.2019.10.010

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