MINIMAX DISTANCE DESIGNS IN 2-LEVEL FACTORIAL-EXPERIMENTS

被引:14
作者
JOHN, PWM
JOHNSON, ME
MOORE, LM
YLVISAKER, D
机构
[1] UNIV TEXAS, AUSTIN, TX 78712 USA
[2] UNIV CENT FLORIDA, ORLANDO, FL 32816 USA
[3] LOS ALAMOS NATL LAB, LOS ALAMOS, NM 87545 USA
[4] UNIV CALIF LOS ANGELES, LOS ANGELES, CA USA
基金
美国国家科学基金会;
关键词
BAYESIAN DESIGN; COMPUTER EXPERIMENTS; DESIGN OPTIMALITY CRITERIA; 2-LEVEL FRACTIONAL FACTORIAL DESIGN;
D O I
10.1016/0378-3758(94)00047-Y
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
A minimax distance criterion was set forth in Johnson et al. (1990) for the purpose of selection among experimental designs. Unlike the usual design criteria such as D-, E- or G-optimality, minimax distance presumes no underlying model and, in turn, is not concerned with the rank of an associated design matrix. In situations where either the model is unknown or it is not possible to run enough experiments to estimate all parameters of an assumed model, this criterion is considered as a viable tool in the task of design selection. This paper deals with the design space associated with n factors, each of which can take two levels. We exhibit minimax distance designs that compare favorably with designs chosen to do well on classical grounds.
引用
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页码:249 / 263
页数:15
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