JSM 2005 Online Program

Abstract #303522

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.

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Activity Number:	28
Type:	Contributed
Date/Time:	Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Physical and Engineering Sciences
Abstract - #303522
Title:	Efficient Experimental Designs for the Estimation of Hyperparameters in Hierarchical Bayes Models
Author(s):	Qing Liu*+
Companies:	The Ohio State University
Address:	3050 St John Court, Columbus, OH, 43202, United States
Keywords:	Experimental Design ; Hierarchical Bayes Model ; hyperparameters ; Efficient Designs ; Design optimality ; parameter estimation
Abstract:	Experimental designs are used to study the cause and effect relationships. A successful design makes efficient use of available resources, leading to more informative inferences. Under the classical linear model specification, traditional full-factorial designs and orthogonal arrays have been applied in designing experiments in the field of marketing. Computerized search has been used to find efficient designs in cases where orthogonal designs are not available. In recent years, Hierarchical Bayes Model has been used as a powerful approach to address consumer heterogeneity and estimation on the hyperparameters of special interest to marketers. It is a much more complex problem to find the efficient designs under this setting, and previous research is limited to special scenarios with restrictive assumptions. We intend to provide efficient designs for the estimation of the hyperparameters under more general scenarios.

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