%0 Journal Article
%T Use of BayesSim and Smoothing to Enhance Simulation Studies
%A Jeffrey D. Hart
%J Open Journal of Statistics
%P 153-172
%@ 2161-7198
%D 2017
%I Scientific Research Publishing
%R 10.4236/ojs.2017.71012
%X 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 <em>models</em> 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 <em>entire parameter space</em>, 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.
%K Loss Function
%K Bayes Risk
%K Prior Distribution
%K Regression
%K Simulation
%K Skew-Normal Distribution
%K Goodness of Fit
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=74503