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 in | Computational statistics & data analysis Vol. 77; pp. 223 - 232 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.09.2014
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ISSN | 0167-9473 1872-7352 |
DOI | 10.1016/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. |
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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 |
Author_xml | – sequence: 1 givenname: Ujjwal surname: Das fullname: Das, Ujjwal email: dsujjwal@gmail.com organization: Division of Statistics, Northern Illinois University, DeKalb, IL 60115, USA – sequence: 2 givenname: Sudhir surname: Gupta fullname: Gupta, Sudhir email: sudhir@math.niu.edu, sudhirgupta.stat@gmail.com organization: Division of Statistics, Northern Illinois University, DeKalb, IL 60115, USA – sequence: 3 givenname: Shuva surname: Gupta fullname: Gupta, Shuva email: sgupta22@ncsu.edu organization: Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA |
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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 |
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