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Buja, A., Hastie, T. and Tibshirani, R. (1989) Linear Smoothers and Additive Models. Annals of Statistics, 17, 453-555. https://doi.org/10.1214/aos/1176347115
has been cited by the following article:
TITLE: Use of BayesSim and Smoothing to Enhance Simulation Studies
AUTHORS: Jeffrey D. Hart
KEYWORDS: Loss Function, Bayes Risk, Prior Distribution, Regression, Simulation, Skew-Normal Distribution, Goodness of Fit
JOURNAL NAME: Open Journal of Statistics, Vol.7 No.1, February 28, 2017
ABSTRACT: 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.
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