JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 75
Type: Contributed
Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #305459
Title: Model-Based Sufficient Dimension Reduction for Longitudinal Data Analysis
Author(s): Shanshan Ding*+ and Dennis Cook
Companies: University of Minnesota-Twin Cities and University of Minnesota
Address: 313 Ford Hall, Minneapolis, MN, 55455, United States
Keywords: Sufficient dimension reduction ; central subspace ; inverse regression ; partial central subspace
Abstract:

Principal components analysis (PCA) and principal fitted components (PFC) are two major dimension reduction methods in regression. In current literature, these two methods are only applied to data with simple structure, where all observations are independently and identically distributed. In many real applications, however, one often needs to deal with data that contain dependent observations, such as repeated measured or longitudinal data. In this paper, we extend classical PCA and PFC and develop sufficient dimension reduction methods for this type of data. The proposed methods can reduce predictors' dimension and preserve full information in the conditional distribution of Y|X. Both methods are built on normal inverse models of the predictors. Thus, they inherit the asymptotic properties from maximum likelihood estimation. Furthermore, the extended PFC models are formed as inverse models of the predictors on the response, which can gain further efficiency by effective use of the response information without slicing. We demonstrate that our proposed methods outperform the existing dimension reduction approaches in both simulation study and real data analysis.


The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program




2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.