Bayesian nonparametric regression models for modeling and predicting healthcare claims

Robert Richardson and Brian Hartman

Insurance: Mathematics and Economics, 2018, vol. 83, issue C, 1-8

Abstract: Standard regression models are often insufficient to describe the complex relationships that exist in healthcare claims. A Bayesian nonparametric regression approach is presented as a flexible regression model that relaxes the assumption of Gaussianity. The details for implementation are presented. Bayesian nonparametric regression is applied to a dataset of claims by episode treatment group (ETG) with a specific focus on prediction of new observations. It is shown that the predictive accuracy improves when compared both to standard linear model assumptions and the more flexible Generalized Beta regression. Of the 347 different ETGs, the nonparametric regression outperformed both the standard linear and generalized beta regression on all but 11. By studying Conjunctivitis and Lung Transplants specifically, it is shown that this approach can handle complex characteristics of the regression error distribution such as skewness, thick tails, outliers, and bimodality.

Keywords: Dependent Dirichlet process; Episode treatment group; Markov chain Monte Carlo; Model comparison; Linear models (search for similar items in EconPapers)
JEL-codes: C11 C46 I11 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:83:y:2018:i:c:p:1-8

DOI: 10.1016/j.insmatheco.2018.06.002

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