Robustness of subset response surface designs to missing observations

被引：29
|
作者：
Ahmad, Tanvir [1 ]
Gilmour, Steven G. [1 ]
机构：
[1] Queen Mary Univ London, Sch Math Sci, London E1 4NS, England
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2010年 / 140卷 / 01期
关键词：
Response surface methodology; Missing observations; Subset design; Minimax loss criterion; Prediction variance; OPTIMIZATION; METHODOLOGY; OIL;
D O I：
10.1016/j.jspi.2009.06.011
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：
Experiments designed to investigate the effect of several factors on a process have wide application in modern industrial and scientific research. Response surface designs allow the researcher to model the effects of the input variables on the response of the process. Missing observations can make the results of a response surface experiment quite misleading, especially in the case of one-off experiments or high cost experiments. Designs robust to missing observations can attract the user since they are comparatively more reliable. Subset designs are studied for their robustness to missing observations in different experimental regions. The robustness of subset designs is also improved for multiple levels by using the minimax loss criterion. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码：92 / 103
页数：12
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