Wednesday, September 9, 2009
04:30 PM in REC 315
Assistant Professor Xiao Wang
Department of Statistics, Purdue University

Nonparametric and Semiparametric Inference with Applications in Astronomy and Reliability

Abstract

This talk deals with nonparametric and semiparametric methods that arise in two major areas of application: astronomy and reliability engineering. In both of these areas, extensive amounts of data are now routinely being collected. Nonparametric and semiparametric methods are especially useful in such environments.

The first part of this talk focuses on mapping the distribution of dark matter in galaxies close to the Milky Way. Current estimates are that majority of matter in the universe is dark, and its physical constitution remains a matter of controversy among astronomers. The problem of dark matter raises many philosophical and methodological questions about the process of confirming scientific hypotheses in contexts where existing theory generates a wide range of alternative explanations of the available empirical data. To address these and related questions, I will introduce a nonparametric method to estimate the dark matter distributions.

The second part of this talk focuses on degradation modeling in reliability. Traditional analysis in reliability focuses on collecting and modeling time-to-failure data. This poses difficulties in high-reliability applications where there are few failures and high degrees of censoring. Fortunately, advances in sensing technologies are making it possible to collect extensive amount of data on degradation and performance-related measures associated with systems and components. I will introduce a semiparametric likelihood method to study different types of Levy processes for degradation data.

Some other interesting theoretical and applied problems will also be discussed if I have time.