Mathematical Sciences: Applied Multivariate Tools

这项研究包括五个主题，旨在开发更好的多变量数据分析工具：(1)回归中子集的选择和评估程序，(2)通过预测变量和预测变量的转换来拟合非线性-非参数加性模型的新的更好的程序，(3)全局非线性优化方法，(4)原型分析算法；(5)非线性平滑方法。这项研究是在统计领域的回归。随着廉价计算能力的出现，数据分析工具得到了扩展，这是对传统一般线性模型的重大突破，使其在实践中变得可行。

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.