Proposal Number: DMS 9804058 PI: David Ruppert Institution: Cornell University Project: Nonparametric Regression Abstract: The research focuses on (a) fitting regression splines by penalized methods, (b) Bayesian nonparametric inference using regression splines, (c) multivariate regression splines with applications to stochastic dynamic programming, and (d) bandwidth selection in local regression. Regression is a statistical technique for discovering relationships between variables, for example, percentage of saturated fat in one's diet and the probability of cancer. Regression is applied thoroughout the sciences and engineering. Modern regression methodology, including this research, focuses on complex nonlinear relationships with large numbers of variables. A typical example would be estimation of credit risk based upon an entire credit history and other financial variables of a credit applicant. This research will be on nonparametric regression meaning that the form of the relationship must be discovered from the data. Nonparametric regression techniques are rapidly finding applications in business under the name "data mining." In a separate project funded by the SRC, the research will be applied to the improvement of tool utilization in semiconductor manufacturing.
