Modeling Different Dependence Structures Involving Count Data With Applications To Insurance Economics And Genetics

Modeling Different Dependence Structures Involving Count Data with Applications to Insurance, Economics and Genetics

Modeling Different Dependence Structures Involving Count Data with Applications to Insurance, Economics and Genetics

This thesis deals with several dependence structures for count responses. These count variables are typically not only overdispersed but also show a large share of zero observations. Based on pair copula constructions a method for sampling from such high-dimensional count random vectors with a specified Pearson correlation will be developed. Temporal dependence structures are investigated based on generalized estimating equations for generalized Poisson variables. In the field of dependent insurance claim totals, a dependence model also based on pair-copula constructions will be developed. The challenge of this problem is that the insurance claims of some of the dependent margins may be zero, and a marginal claim size distribution will therefore not fit in the general framework of copula modeling.

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