JSM 2005 Online Program

Abstract #302304

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:	45
Type:	Invited
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #302304
Title:	A Bayesian Analysis of Two-way Categorical Data from Small Areas Incorporating Intraclass Correlation
Author(s):	Balgobin Nandram*+ and Jai W. Choi
Companies:	Worcester Polytechnic Institute and National Center for Health Statistics
Address:	Department of Mathematical Sciences, Worcester, MA, 01609,
Keywords:	Bayes factor ; Clustered data ; Gibbs sampler ; Hierarchical Bayesian model ; Surrogate sampling
Abstract:	It is straight forward to analyze data from several two-way categorical tables when each table represents data from an area. For each table, the common chi-squared test of independence between the two variables and maximum likelihood estimators of the cell probabilities are readily available. However, when the counts in the two-way categorical tables are formed from familial data (clusters of correlated data), the common chi-squared test no longer applies and the cell probabilities cannot be efficiently estimated. We consider the situation in which the intraclass correlation varies with the size of the cluster, but it is the same for all clusters of the same size. In addition, when the data from each table are sparse, inference for many of the individual tables become inefficient. Our method, an extension of the one in Nandram and Choi (2004) for a single area, permits a ``borrowing of strength'' across the tables via a hierarchical Bayesian model. It is assumed the intraclass correlations and the cell probabilities vary across areas and that they have the same joint distribution over areas. We use Markov Chain Monte Carlo methods to fit our 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