Lu Yang (University of Minnesota)
Title: Dependence Modeling of Mixed Insurance Claim Data
Abstract:
Multivariate claim data are common in insurance applications, e.g. claims of each policyholder for different types of insurance coverages. Understanding the dependencies among such multivariate risks is essential for the solvency and profitability of insurers. Effectively modeling insurance claim data is challenging due to their special complexities. At the policyholder level, claims data usually follow a two-part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long-tailed distribution. Copula models are often employed in order to simultaneously model the relationship between outcomes and covariates while flexibly quantifying the dependencies among the different outcomes. However, due to the mixed data feature, specification of copula models has been a problem. We fill this gap by developing a consistent nonparametric copula estimator for mixed data. Under our framework, both the models for the i) marginal relationship between covariates and claims and ii) dependence structure between claims can be chosen in a principled way. We show the uniform convergence of the proposed nonparametric copula estimator. Using the claim data from the Wisconsin Local Government Property Insurance Fund, we illustrate that our nonparametric copula estimator can assist analysts in identifying important features of the underlying dependence structure, revealing how different claims or risks are related to one another.
Speaker
Lu Yang is an Assistant Professor in the School of Statistics at the University of Minnesota. Her research has focused on the development of statistical methodology motivated by insurance applications. In particular, she is interested in multivariate analysis with discrete outcomes, and she has worked on nonparametric estimation of copulas. Her recent research also includes regression model diagnostics, especially with discrete and semi-continuous outcomes.
Meeting ID: 843 0865 5572
Passcode: 690084
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