JSM 2005 Online Program

Abstract #302393

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	335
Type:	Invited
Date/Time:	Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor:	IMS
Abstract - #302393
Title:	Analyzing Generalized Longitudinal Data with Latent Gaussian Processes and Functional Principal Components
Author(s):	Hans-Georg Mueller*+ and Peter Hall and Fang Yao
Companies:	University of California, Davis and Australian National University and Colorado State University
Address:	Dept. of Statistics, Davis, CA, 95616,
Keywords:	functional data analysis ; principal components ; longitudinal data ; Gaussian process
Abstract:	In longitudinal data analysis, one may encounter nonGaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be discrete, such as binomial or Poisson, or continuous. The timings of the repeated measurements often are sparse and irregular. In this talk, we introduce a latent Gaussian process model for this situation and propose a method to infer its properties. This enables us to develop a version of functional principal component analysis for such data. The prediction of individual trajectories from sparse observations is demonstrated. The predicted functional principal component scores can be used for further statistical analysis, such as regression or clustering. The proposed methods are nonparametric and computationally fast, and are illustrated with biomedical longitudinal data.

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