Abstract #300119

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 #300119
Activity Number: 401
Type: Invited
Date/Time: Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
Sponsor: JASA, Applications and Case Studies
Abstract - #300119
Title: Wavelet-Based Nonparametric Modeling of Hierarchical Functions in Colon Carcinogenesis
Author(s): Jeffrey S. Morris*+ and Marina Vannucci and Philip J. Brown and Raymond J. Carroll
Companies: University of Texas M.D. Anderson Cancer Center and Texas A&M University and University of Kent, Canterbury and Texas A&M University
Address: 1515 Holcombe Blvd., Houston, TX, 77030-4009,
Keywords: Bayesian methods ; carcinogenesis ; functional data analysis ; model averaging ; nonparametric regression ; wavelets
Abstract:

We develop new methods for analyzing data from an experiment using rodent models to investigate the effect of type of dietary fat on MGMT, an important biomarker in early colon carcinogenesis. The data consist of observed profiles contained within a two-stage hierarchy, a structure we dub "hierarchical functional data." We present a new method providing a unified framework for modeling these data, simultaneously yielding estimates and posterior samples for mean, individual, and subsample-level profiles, as well as covariance parameters at the various hierarchical levels. Our method is nonparametric in that it does not require the prespecification of parametric forms for the functions, and involves modeling in the wavelet space, which is effective for spatially heterogeneous functions as encountered in our data. Our approach is Bayesian, with the only informative hyperparameters in our model being effectively smoothing parameters. Analysis of this dataset yields interesting new insights into how MGMT operates in early colon carcinogenesis and may depend on diet. Our method is general so can be applied to other data sets where correlated functional data are encountered.

  • 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