Abstract #301373

This is the preliminary program for the 2003 Joint Statistical Meetings in San Francisco, California. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 2-5, 2003); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

Back to main JSM 2003 Program page
JSM 2003 Abstract #301373
Activity Number: 450
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301373
Title: Functional Response Models and Their Applications
Author(s): Xin Tu*+
Companies: University of Pennsylvania
Address: Dept. of Biostatistics & Clinical Epi., Philadelphia, PA, 10104,
Keywords: structural equation models ; U-statistics ; correlated correlations ; distribution-free regression models ; generalized estimating equation ; Mann-Whitney-Wilcoxon test
Abstract:

We introduce a new class of semiparametric (distribution-free) regression models with functional responses. This class of functional response models (FRM) generalizes the traditional regression models by defining the response variable as a function of several responses from multiple subjects. By using such multiple-subjects-based responses, the FRM not only integrates some popular nonparametric and semiparametric approaches within a unified modeling framework, but also provides a platform for developing new models for addressing limitations of existing nonmodels and semimodels. For example, by viewing the popular nonparametric two-sample Mann-Whitney-Wilcoxon as a regression under FRM, we can readily generalize it to account for multiple groups and to examine second-order variability of the distributions. The FRM is also quite effective in addressing limitations of parametric models. For example, the linear mixed-effects model and the structural equation model are popular in psychosocial research. By developing new semiparametric approaches under FRM, we can provide robust estimates for both the population and cluster specific parameters.

  • 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 2003 program

JSM 2003 For information, contact meetings@amstat.org or phone (703) 684-1221. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2003