Simulated Annealing Model Search for Subset Selection in Screening Experiments

被引：16

作者：

Wolters, Mark A. ^{[1
]}

Bingham, Derek ^{[2
]}

机构：

[1] Univ Western Ontario, Dept Stat & Actuarial Sci, London, ON N6A 5B7, Canada

[2] Simon Fraser Univ, Dept Stat & Actuarial Sci, Burnaby, BC V5A 1S6, Canada

来源：

TECHNOMETRICS | 2011年 / 53卷 / 03期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Linear regression; Model selection; Nonregular factorial design; BAYESIAN VARIABLE-SELECTION; DESIGNED EXPERIMENTS;

D O I：

10.1198/TECH.2011.08157

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：

The analysis of screening experiments based on nonregular designs can lead to a model selection problem in which the number of variables is large, the number of trials is small, and there are constraints on model structure. Common subset selection methods do not perform well in this setting. We propose a new approach particularly well suited to screening. The method uses an intentionally nonconvergent stochastic search to generate a large set of well-fitting models, each with the same number of variables. Model selection is then viewed as a feature extraction problem from this set. An easy-to-use graphical method and an automatic approach are proposed to determine the best models. Computer code and additional supplementary materials are available online.

引用

页码：225 / 237

页数：13

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