JSM 2005 - Toronto

Abstract #304401

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: 258
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304401
Title: A Simulation-based Approach to Bayesian Sample Size Determination: Examples and Issues
Author(s): Fei Wang*+
Companies: Boston University
Address: 200 Springs Road 152, Bedford, MA, 01730, United States
Keywords: Bayesian Sample Size Determination ; Hierarchical Modeling ; Multi-reader ROC analysis ; Model Performance Criterion ; Model Selection Criterion ; ALC
Abstract:

Sample size determination (SSD) is an important step in experimental design. Practitioners often face many uncertainties at the design stage, and Bayesian methods to determine sample sizes are ideally suited for this design aspect. First, valuable information usually is available prior to the experiment. It is helpful to incorporate this prior information at the design stage. In fact, "sampling prior" specifications in Bayesian SSD will arise in a "what if" spirit, allowing designers to assess Bayesian learning as a function of a sample size over a range of design situations. Second, it is sensible to average over the sample space because the sample has not yet been observed and the general principle of averaging over what is unknown applies. In this talk, we briefly review two types of Bayesian SSD approaches: simulation-based and formulated under Bayesian hierarchical modeling framework. We use examples from multireader study in evaluating diagnostic tests. Computational issues and software availability also will be addressed.

  • 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

JSM 2005 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2005