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DESCRIPTION:Title: Dependence Modeling of Mixed Insurance Claim Data\n\nAbs
 tract:\n\nMultivariate 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 essentia
 l for the solvency and profitability of insurers. Effectively modeling ins
 urance claim data is challenging due to their special complexities. At the
  policyholder level\, claims data usually follow a two-part mixed distribu
 tion: a probability mass at zero corresponding to no claim and an otherwis
 e positive claim from a skewed and long-tailed distribution. Copula models
  are often employed in order to simultaneously model the relationship betw
 een outcomes and covariates while flexibly quantifying the dependencies am
 ong the different outcomes. However\, due to the mixed data feature\, spec
 ification of copula models has been a problem. We fill this gap by develop
 ing a consistent nonparametric copula estimator for mixed data. Under our 
 framework\, both the models for the i) marginal relationship between covar
 iates and claims and ii) dependence structure between claims can be chosen
  in a principled way. We show the uniform convergence of the proposed nonp
 arametric 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 ri
 sks are related to one another.\n\n\n	Speaker\n\n\nLu 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 motivat
 ed by insurance applications. In particular\, she is interested in multiva
 riate analysis with discrete outcomes\, and she has worked on nonparametri
 c estimation of copulas. Her recent research also includes regression mode
 l diagnostics\, especially with discrete and semi-continuous outcomes.\n\n
 Zoom Link\n\nMeeting ID: 843 0865 5572\n\nPasscode: 690084\n\n \n\n \n
DTSTART:20210409T193000Z
DTEND:20210409T203000Z
SUMMARY:Lu Yang (University of Minnesota)
URL:/mathstat/channels/event/lu-yang-university-minnes
 ota-330307
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