Analysis Methods for Supersaturated Design: Some Comparisons Article Swipe
Runze Li
,
Dennis K. J. Lin
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.6339/jds.2003.01(3).134
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.6339/jds.2003.01(3).134
Supersaturated designs are very cost-effective with respect to the number of runs and as such are highly desirable in many preliminary studies in industrial experimentation.Variable selection plays an important role in analyzing data from the supersaturated designs.Traditional approaches, such as the best subset variable selection and stepwise regression, may not be appropriate in this situation.In this paper, we introduce a variable selection procedure to screen active effects in the SSDs via nonconvex penalized least squares approach.Empirical comparison with Bayesian variable selection approaches is conducted.Our simulation shows that the nonconvex penalized least squares method compares very favorably with the Bayesian variable selection approach proposed in Beattie, Fong and Lin (2001).
Related Topics
Concepts
Selection (genetic algorithm)
Feature selection
Variable (mathematics)
Computer science
Bayesian probability
Design matrix
Mathematical optimization
Partial least squares regression
Least-squares function approximation
Regression analysis
Mathematics
Statistics
Machine learning
Artificial intelligence
Estimator
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.6339/jds.2003.01(3).134
- https://jds-online.org/journal/JDS/article/1151/file/pdf
- OA Status
- diamond
- Cited By
- 76
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2182658016
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2182658016Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.6339/jds.2003.01(3).134Digital Object Identifier
- Title
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Analysis Methods for Supersaturated Design: Some ComparisonsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-21Full publication date if available
- Authors
-
Runze Li, Dennis K. J. LinList of authors in order
- Landing page
-
https://doi.org/10.6339/jds.2003.01(3).134Publisher landing page
- PDF URL
-
https://jds-online.org/journal/JDS/article/1151/file/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://jds-online.org/journal/JDS/article/1151/file/pdfDirect OA link when available
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Selection (genetic algorithm), Feature selection, Variable (mathematics), Computer science, Bayesian probability, Design matrix, Mathematical optimization, Partial least squares regression, Least-squares function approximation, Regression analysis, Mathematics, Statistics, Machine learning, Artificial intelligence, Estimator, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
76Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 5, 2022: 3, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| countries_distinct_count | 1 |
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| citation_normalized_percentile.value | 0.88931434 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |