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Article citations


Lee, Y., Lin, Y. and Wahba, G. (2004) Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and Satellite Radiance Data. Journal of the American Statistical Association, 99, 67-81.

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.