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.
