Department of Statistics Seminar
North Carolina State University

presents

Dr. Wanli Min

IBM Corporation

"Inference on time series generated from weakly dependent noise "

ABSTRACT

In this talk, we consider two aspects of statistical inference on certain time series generated by a sequence of weakly dependent noise, namely, model identification and model selection. Sample (partial) autocorrelation functions plays an important role in model identification. The asymptotic behavior of (P)ACF of a linear process with iid innovations has been studied extensively. We will consider the same problem for general linear processes with dependent noise such as Threshold AutoRegressive (TAR) and Generalized AutoRegressive Conditional Heteroscadestic (GARCH) processes. Central limit theorems and invariance principles are established under mild conditions within a new framework without mixing conditions. Information criteria is widely used to do model selection in time series analysis. We show the difficulties of existing criteria, such as AICc, AIC and BIC in the presence of weakly dependent innovations. We propose a modified AIC and show its efficiency and robustness through simulations.

Friday, September, 16, 2005

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.