Use of BayesSim and Smoothing to Enhance Simulation Studies
作者: Jeffrey D. Hart
刊名: Open Journal of Statistics, 2017, Vol.7 (1), pp.153-172
来源数据库: Scientific Research Publishing Journal
DOI: 10.4236/ojs.2017.71012
原始语种摘要: 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...
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  • nonparametric 非参量的
  • approach 
  • methodology 方法学
  • goodness 优势
  • parameter 参数
  • interest 兴趣
  • model 模型
  • distribution 分布
  • opposed 对面
  • hundreds 亚麻织物经密