Department Seminars

General Information

When: Thursdays from 4:00–4:50 p.m.
Where: 1170 TMCB

Gilbert W. Fellingham
Brigham Young University
Department of Statistics

2007-09-13

Parametric and Nonparametric Bayesian Methods to Model Health Insurance Claims Costs

In this presentation, I will discuss Bayesian parametric and nonparametric hierarchical approaches to modeling health insurance claims data. Both prediction methods produce credibility type estimators (Herzog, 1999, or Venter, 1996), which use relevant information from related experience. In the parametric model, the likelihood arises through a mixture of a gamma distribution for the non-zero costs (severity) with a point mass for the zero costs (propensity). In the nonparametric extension, Dirichlet process priors are associated with the propensity parameters as well as the severity parameters. Posterior inference and prediction for both models is based on Markov chain Monte Carlo posterior simulation methods. A simulation study is used to demonstrate the utility of the nonparametric model across different settings. Moreover, we illustrate the methodology using real data provided by a major medical provider from a block of medium sized groups in the Midwest from 1994 and 1995. The models were fitted to the 1994 data, with their performance assessed and compared using the 1995 data.