Statistical Modelling 16 (4) (2016), 261–278
Bayesian semiparametric density ratio modelling with applications to medical malpractice reform
Kevin D Dayaratna
The Heritage Foundation,
Center for Data Analysis,
Washington, DC,
United States
e-mail: kevin.dayaratna@heritage.org
Benjamin Kedem
Department of Mathematics,
University of Maryland,
College Park, MD,
USA
Abstract:
This study examines the efficacy of tort reforms instituted throughout the country during the last decade, improving upon existing semiparametric density ratio estimation (DRE) methodologies in the process. DRE is a well-known semiparametric modelling technique that has been used for well over two decades. Although the approach has been demonstrated to be extremely useful in statistical modelling, it has suffered from one main limitation—the methodology has thus far not been capable of modelling individual-level heterogeneity. We address this issue by presenting a novel adaptation of DRE to model individual level heterogeneity. We do so by marginalizing the associated empirical likelihood function involving density ratios to provide an overall distribution of the entire population despite having extremely limited initial information about each individual in the dataset. We apply this approach to medical malpractice loss data from the previous decade to quantify the probability of changes in tort losses. Our results demonstrate the success of a number of recently implemented malpractice reforms. Comparisons to existing DRE methods, as well as standard regression methods, illustrate the efficacy of our approach.
Keywords:
Bayesian computation; Density ratio estimation; medical malpractice reform; semiparametric modelling; tort reform.
Downloads:
Example data in
zipped archive. The data come from:
Crain, N., Crain, M., McQuillan, L. J., and Abramyan, H. (2009). Tort law tally: How state tort reforms affect tort losses and tort insurance premiums. Pacific Research Institute, San Francisco, CA.
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