Selecting baseline two-level designs using optimality and aberration criteria when some two-factor interactions are important

Author: 
Date created: 
2019-06-14
Identifier: 
etd20325
Keywords: 
A-optimality
Baseline parameterization
Bias
D-optimality
Minimum aberration
Search algorithm
Abstract: 

The baseline parameterization is less commonly used in factorial designs than the orthogonal parameterization. However, the former is more natural than the latter when there exists a default or preferred setting for each factor in an experiment. The current method selects optimal baseline designs for estimating a main effect model. In this project, we consider the selection of optimal baseline designs when estimates of both main effects and some two-factor interactions are wanted. Any other potentially active effect causes bias in estimation of the important effects. To minimize the contamination of these potentially active effects, we propose a new minimum aberration criterion. Moreover, an optimality criterion is used to minimize the variances of the estimates. Finally, we develop a search algorithm for selecting optimal baseline designs based on these criteria and present some optimal designs of 16 and 20 runs for models with up to three important two-factor interactions.

Document type: 
Graduating extended essay / Research project
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Boxin Tang
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: