Tuesday, May 22 at 11 am

Hira L. Koul, Professor Department of Statistics and Probability, Michigan State University Title: Model Diagnostics via Martingale Transforms

Classical problems in statistics are to fit a distribution up to unknown location-scale parameters and to fit a parametric model to the regression-autoregressive function. The first problem is generic to many other statistical models including the celebrated regression and autoregressive and generalize autoregressive conditionally heteroscedastic (ARCH-GARCH) models where one is testing that innovations are from a given distribution. It will be argued that the Khamaladze's martingale transformation of the residual empirical process that yields asymptotically distribution free tests for the one sample location-scale model does the same thing for a parametric heteroscedastic regression model, and ARCH-GARCH models. Analogous tests for the second problem will be also discussed.

Created by Noelle I. Samia
Last Updated 03/27/2007