Mathematical Sciences: Statistical Model Building with Generalized Splines

本研究将涉及样条函数。 样条 函数及其在多个变量中的各种推广 已被证明是非常有用的建立经验模型， 描述大型数据集中的多变量物理现象。 的 该项目的一般方法是发展理论 所提出的半参数模型的性质，以评估如何 他们可能会在蒙特卡洛实验中 仿真研究，最后，对选定的方法进行尝试 “真实的”实验数据集。 该项目属于一般统计领域， 专注于高速计算。 样条函数模型需要 新的统计和数字技术，让数据选择 从各种可能的模型中，以适应这些模型，并 提供尽可能多的信息，说明模型的准确性 预测很可能是。 这项研究的大部分支持， AFSOR和NASA。 这个奖项支持一个研究生工作 项目中。

This research will involve spline functions. Spline functions and their various generalizations in several variables have proved to be very useful for building empirical models which describe multivariate physical phenomena in large data sets. The general approach of this project is to develop theoretical properties of the proposed semi-parametric models, to assess how well they might be expected to work in practice by Monte Carlo simulation studies, and finally, to try the methods on selected "real" experimental data sets. This project is in the general area of statistics and focuses on high speed computing. The spline function models need new statistical and numerical techniques to let the data select from among various possible models, to fit these models, and to provide as much information as possible on how accurate the model forecast are likely to be. Most of this research is supported by AFSOR and NASA. This award supports a graduate student to work in the project.