Robust Minimum Distance Mixture and Regression Modelin
Dr. David Scott
Monday, 11 October 2004
The covariance matrix is a key component of many multivariate robust procedures, whether or not the data are assumed to be Gaussian. We examine the idea of robustly fitting a mixture of multivariate Gaussian densities, but when the number of components is intentionally too few. Using a minimum distance criterion, we show how useful results may be obtained in practice. Application areas are numerous, and examples will be provided. We will reexamine the classical Boston housing data, with spatial views of the residuals.
Refreshments will be served at 3:45 p.m. in Room 008 of the Statistics Building