JSM 2005 - Toronto

Abstract #304683

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 183
Type: Topic Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #304683
Title: Time-frequency Functional Linear Models
Author(s): Li Qin*+ and Wensheng Guo and Brian Litt
Companies: Fred Hutchinson Cancer Research Center and University of Pennsylvania and University of Pennsylvania
Address: 1100 Fairview Ave N Mailstop LE400, Seattle, WA, 98109, United States
Keywords: Epilepsy ; Locally Stationary Process ; Smoothing Spline ; State Space Model ; Time Series ; Varying Coefficient Model
Abstract:

Traditional time series analysis focuses on one long time series, while in biomedical experiments that collect many time series, the research interest is usually on how the covariates impact the patterns of stochastic variations over time. In this paper, we propose time-frequency functional linear models in which a time series is the basic unit in the data analysis. Because the stochastic variation of a time series is uniquely characterized by its spectrum, we propose a varying coefficient model for the spectra. These models can be used to compare groups of time series and to estimate the covariate effects. We focus our development on locally stationary time series that include stationary time series as special cases. A two-stage procedure is proposed for estimation. An equivalent state space model is constructed as an efficient estimation tool. The proposed method is applied to the epileptic electroencephalogram (EEG) data.

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Revised March 2005