Author(s)

Jeffrey D. Hart

Department of Statistics, Texas A&M University, College Station, TX, USA.

The conventional form of statistical simulation proceeds by selecting a few models and generating hundreds or thousands of data sets from each model. This article investigates a different approach, called BayesSim, that generates hundreds or thousands of models from a prior distribution, but only one (or a few) data sets from each model. Suppose that the performance of estimators in a parametric model is of interest. Smoothing methods can be applied to BayesSim output to investigate how estimation error varies as a function of the parameters. In this way inferences about the relative merits of the estimators can be made over essentially the entire parameter space, as opposed to a few parameter configurations as in the conventional approach. Two examples illustrate the methodology: One involving the skew-normal distribution and the other nonparametric goodness-of-fit tests.

Loss Function, Bayes Risk, Prior Distribution, Regression, Simulation, Skew-Normal Distribution, Goodness of Fit

Hart, J. (2017) Use of BayesSim and Smoothing to Enhance Simulation Studies. Open Journal of Statistics, 7, 153-172. doi: 10.4236/ojs.2017.71012.