Abstract #301311

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 #301311
Activity Number: 205
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301311
Title: Bayesian Isotonic Regression and Trend Analysis
Author(s): Brian H. Neelon*+ and David B. Dunson
Companies: University of North Carolina and National Institute of Environmental Health Sciences
Address: 87 Granger Road, Chapel Hill, NC, 27516-4513,
Keywords: additive model ; autoregressive prior ; constrained estimation ; order restricted inference ; piecewise linear model ; trend test
Abstract:

In many applications, the mean of a response variable can be assumed to be a non-decreasing function of a continuous predictor, controlling for other covariates. In such cases, interest often focuses on estimating the response function, while also assessing evidence of an association. This article proposes a new framework for Bayesian isotonic regression and order restricted inference based on a constrained piecewise linear model with unknown knot locations. The nondecreasing constraint is incorporated through a prior distribution consisting of a product mixture of point masses (accounting for flat regions) and truncated autoregressive normal densities. This prior density is conditionally conjugate, resulting in simplified posterior computation via a Markov chain Monte Carlo algorithm. Generalizations to accommodate multiple predictors through an additive modeling structure are described, and the approach is applied to data from a study of DDE exposure and birth weight.

  • 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