JSM 2005 Online Program

Abstract #303101

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:	51
Type:	Invited
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	Section on Survey Research Methods
Abstract - #303101
Title:	Estimation of Prevalence of Overweight in Small Areas: A Robust Extension of Fay-Herriot Model
Author(s):	Dawei Xie*+
Companies:	University of Pennsylvania
Address:	617 Blockley Hall, Philadelphia, PA, 19104, United States
Keywords:	Hierarchical Bayesian model ; Complex Sample Survey ; linear mixed effect model
Abstract:	Hierarchical model such as Fay-Herriot (FH) often is used to develop small-area estimates. It might perform well overall, but it's vulnerable to outliers. We propose two ways to detect outliers under the FH model. One is the posterior predictive distribution approach; the other uses a half-normal plot. We then propose a robust extension of Fay-Herriot model that assumes the area random effects follow a t-distribution with an unknown degree of freedom. The inference is done in a Bayesian framework. Markov chain Monte Carlo (MCMC) such as Gibbs sampling and Metropolis-Hastings acceptance and rejection algorithms is used to obtain the posterior distribution of small-area estimates and model parameters. The differences between the estimates from the FH model and the t model are discussed. The procedure is illustrated in obtaining the prevalence of overweight for the state of Alaska, District of Columbia, and 1,051 counties identified in the public use Behavioral Risk Factor Surveillance System (BRFSS) data for 2003.

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