Date & Time: Wednesday, January 28, 2009, 15:00-16:00.
Venue: Ramanujan Hall
Title: Model Diagnostics via Martingale Transforms
Speaker: Hira L. Koul, Michigan State University
Abstract: 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.
This talk is based on some ongoing joint work with Estate Khmaladze.