Efficient Classification for Longitudinal Data

• Activity Number: 189
• Type: Invited
• Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Xianlong Wang*+ and Peiyong (Annie) Qu
• Companies: Fred Hutchinson Cancer Research Center and University of Illinois at Urbana-Champaign
• Keywords: classification method ; machine learning ; quadratic inference functions ; longitudinal data analysis ; generative classifier

Abstract

We propose a new classification method for longitudinal data based on a semiparametric approach. Our approach builds a classifier by taking advantage of modeling information between response and covariates for each class, and assigns a new subject to the class with the smallest quadratic distance. This enables one to overcome the difficulty in estimating covariance matrices while still incorporate correlation into the classifier. Extensive simulation studies and real data applications show that our approach outperforms support vector machine, the logistic regression and linear discriminant analysis for continuous outcomes, and outperforms the naive Bayes classifier, decision tree and logistic regression for discrete responses. We will also present its statistical learning theory including upper bound and normal approximation to the generalization error.