Regular fractional factorial designs with minimum aberration and maximum estimation capacity
成果类型：
Article
署名作者：
Cheng, CS; Mukerjee, R
署名单位：
University of California System; University of California Berkeley; Indian Institute of Management (IIM System); Indian Institute of Management Calcutta
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
发表日期：
1998
页码：
2289-2300
关键词：
摘要：
Using the approach of finite projective geometry, we make a systematic study of estimation capacity, a criterion of model robustness, under the absence of interactions involving three or more factors. Some general results, providing designs with maximum estimation capacity, are obtained. In particular, for two-level factorials, it is seen that constructing a design with maximum estimation capacity calls for choosing points from a finite projective geometry such that the number of lines is maximized and the distribution of these lines among the chosen points is as uniform as possible. We also explore the connection with minimum aberration designs under which the sizes of the alias sets of two-factor interactions which are not aliased with main effects are the most uniform possible.