Mathematical Sciences: Applied Multivariate Tools

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This research encompasses five topics aimed at the development of better working tools for the analysis of multivariate data: (1) Procedures for subset selection and evaluation in regression, (2) A new and better procedure for fitting nonlinear-nonparametric additive models through transformations of the predicted and the predictor variables, (3) Global nonlinear optimization methods, (4) Prototype analysis algorithms, and (5) Nonlinear smoothing methods. This research is in the statistical area of regression. Data analytic tools are extended following a significant step beyond traditional general linear models that was made practically feasible with the availability of cheap computational horsepower.