JSM 2005 Online Program

Abstract #303579

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:	127
Type:	Topic Contributed
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Nonparametric Statistics
Abstract - #303579
Title:	Judgment Poststratification for Designed Experiments
Author(s):	Juan Du*+ and Steven N. MacEachern
Companies:	The Ohio State University and The Ohio State University
Address:	404 Cockins Hall, Columbus, OH, 43210,
Keywords:	contrast estimation ; order restricted randomization ; ranked set sampling ; variance reduction
Abstract:	In many scientific studies, information not easily translated into covariates is ignored in the analysis. However, this type of information may significantly improve inference. In this research, we apply the idea of judgment poststratification to utilize such information. Specifically, we consider experiments conducted under a completely randomized design. Sets of experimental units are formed and the units in a set are ranked. Estimation is performed conditional on the sets and ranks. We propose a new estimator for a treatment contrast and show it is the generalized least squares estimator. We develop asymptotic distribution theory and corresponding inferential procedures for this estimator. We also discuss inference based on randomization theory. We use simulation studies to quantify the superiority of the new estimator and to show its desirable properties for small and moderate sample sizes.

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