|
Activity Number:
|
281
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 14, 2002 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Nonparametric Statistics*
|
| Abstract - #301360 |
|
Title:
|
Optimal Treatment Selection in Designed Experiments
|
|
Author(s):
|
Alan Polansky*+
|
|
Affiliation(s):
|
Northern Illinois University
|
|
Address:
|
, De Kalb, Illinois, 60115, United States of America
|
|
Keywords:
|
bootstrap ; problem of regions ; multiple comparisons ; best subset selection
|
|
Abstract:
|
A common question in experimentation is concerned with finding an optimal level for a treatment using data obtained from a designed experiment. Basing such a determination directly on estimated treatment effects is unreliable because of the inherent variability associated with the estimates. Other methods, such as multiple comparison procedures and best subset selection procedures could also be applied to this problem. Unfortunately, such procedures are often difficult interpret and rely on assumptions that may not be suitable for the experiment under consideration. In this paper, we develop a method of assigning a level of confidence to each treatment that measures how confident we are that each treatment is optimal. The method for assigning the confidence levels is based on a more general methodology developed for the problem of regions, where one assigns levels of confidence that an unknown parameter is within specified subsets of the parameter space. The actual computation of the confidence levels is based on the bootstrap.
|
