Department of Statistics Seminar
North Carolina State University

presents

Dr. David Scott

Rice University

"Remarks on Fitting Mixtures of Regressions and Handling Outliers in Data and Regression"

ABSTRACT

Maximum likelihood estimation is universal but sensitive to model misspecification and outliers. In this talk, I describe an alternative approach to formulating some robust estimates. Robust estimation provides a powerful solution to practical problems in applied statistics. Simple tasks such as data cleaning may be prohibitively expensive with large datasets. In our formulation, maximum likelihood is replaced by a data-based minimum-distance criterion. The usual M-estimator specification of the shape and scale of the influence function is replaced by a single choice of a distribution function for the data. This idea is illustrated for several common choices of data, including Gaussian, with one, several, and many outliers. Similar ideas have application in regression. Here, interest arises not only with the case of outlier-contaminated regression but also in the case of mixtures of regressions, with outliers.

Friday, January 17, 2003

3:35 - 4:35 pm

206 Cox Hall

Refreshments will be served on the second floor of Dabney Hall (left of Room 222) at 3:00 pm.