Mathematical Sciences: Computationally Intensive Problems in Statistics

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This research considers computationally intensive methods in statistics that involve the use of high speed workstations. One problem involves the combined application of data-analytic methods of nonparametric regression and empirical Bayes techniques to improved fitting and prediction for growth curve data. Another problem involves mixed models incorporating both fixed and random effects for binary data. In this problem the focus is on variance components estimation in mixed models and the analogues of best linear unbiased prediction of the observed values of the random effects. These will include models for the repeated measures and longitudinal data settings.