JSM 2005 Online Program

Abstract #304612

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.

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.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	64
Type:	Contributed
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #304612
Title:	Bayesian Semiparametric Inference Based on Ranks in Linear Models
Author(s):	Xiaojiang Zhan*+
Companies:	Merck & Co., Inc.
Address:	1312 Forest View Dr, Avenel, NJ, 07001, United States
Keywords:	linear models ; Bayesian analysis ; robust estimate ; rank estimate
Abstract:	When prior information exists, it would be desirable to incorporate it in the data analysis, even when we are using robust rank-based methods. We discuss the implementation of nonparametric rank-based procedures in the Bayesian context. We summarize the information in a sample of data via the distribution of rank-based quantity and use that distribution as a pseudo-likelihood. Meanwhile, we suppose a prior distribution for the parameter(s) of interest in the unknown function. By Bayes' theorem, we can obtain the complete posterior distribution (or the posterior distribution up to a normalizing constant) of the parameter(s) given the rank-based quantity. Statistical inference then proceeds based on this posterior distribution. Current applications are one-sample and two-sample location models as well as linear models. In this presentation, we focus on the application to linear models, especially the linear regression model.

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 2005 program