Screening active factors in supersaturated designs

Identification of active factors in supersaturated designs (SSDs) has been the subject of much recent study. Although several methods have been previously proposed, a solution to the problem beyond one or two active factors still seems to be unsatisfactory. The smoothly clipped absolute deviation (S...

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Published inComputational statistics & data analysis Vol. 77; pp. 223 - 232
Main Authors Das, Ujjwal, Gupta, Sudhir, Gupta, Shuva
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.09.2014
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ISSN0167-9473
1872-7352
DOI10.1016/j.csda.2014.02.023

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Abstract Identification of active factors in supersaturated designs (SSDs) has been the subject of much recent study. Although several methods have been previously proposed, a solution to the problem beyond one or two active factors still seems to be unsatisfactory. The smoothly clipped absolute deviation (SCAD) penalty function for variable selection has nice theoretical properties, but due to its nonconvex nature, it poses computational issues in model fitting. As a result, so far it has not shown much promise for SSDs. Another issue regarding its inefficiency, particularly for SSDs, has been the method used for choosing the SCAD sparsity tuning parameter. The selection of the SCAD sparsity tuning parameter using the AIC and BIC information criteria, generalized cross-validation, and a recently proposed method based on the norm of the error in the solution of systems of linear equations are investigated. This is performed in conjunction with a recently developed more efficient algorithm for implementing the SCAD penalty. The small sample bias-corrected cAIC is found to yield a model size closer to the true model size. Results of the numerical study and real data analyses reveal that the SCAD is a valuable tool for identifying active factors in SSDs.
AbstractList Identification of active factors in supersaturated designs (SSDs) has been the subject of much recent study. Although several methods have been previously proposed, a solution to the problem beyond one or two active factors still seems to be unsatisfactory. The smoothly clipped absolute deviation (SCAD) penalty function for variable selection has nice theoretical properties, but due to its nonconvex nature, it poses computational issues in model fitting. As a result, so far it has not shown much promise for SSDs. Another issue regarding its inefficiency, particularly for SSDs, has been the method used for choosing the SCAD sparsity tuning parameter. The selection of the SCAD sparsity tuning parameter using the AIC and BIC information criteria, generalized cross-validation, and a recently proposed method based on the norm of the error in the solution of systems of linear equations are investigated. This is performed in conjunction with a recently developed more efficient algorithm for implementing the SCAD penalty. The small sample bias-corrected cAIC is found to yield a model size closer to the true model size. Results of the numerical study and real data analyses reveal that the SCAD is a valuable tool for identifying active factors in SSDs.
Author Das, Ujjwal
Gupta, Shuva
Gupta, Sudhir
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Keywords Effect heredity
Nonconvex penalty
SCAD
Corrected AIC
Smoothly clipped absolute deviation
Shrinkage estimation
Dantzig selector
Sparsity tuning parameter
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Snippet Identification of active factors in supersaturated designs (SSDs) has been the subject of much recent study. Although several methods have been previously...
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SubjectTerms Algorithms
Computation
Corrected AIC
Dantzig selector
Data processing
Design engineering
Design factors
Effect heredity
equations
Mathematical models
Nonconvex penalty
SCAD
screening
Shrinkage estimation
Smoothly clipped absolute deviation
Sparsity tuning parameter
Tuning
Title Screening active factors in supersaturated designs
URI https://dx.doi.org/10.1016/j.csda.2014.02.023
https://www.proquest.com/docview/1551058985
https://www.proquest.com/docview/2237528396
Volume 77
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