Selection of Two-Level Supersaturated Designs for Main Effects Models
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2022/09/13
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Journal
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Indian UGC (journal)
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Refrences26
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Citations4
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Rakhi Singh University of North Carolina at Greensboro, Greensboro, NC
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John Stufken University of North Carolina at Greensboro, Greensboro, NC
Cite
Singh, Rakhi, and John Stufken. “Selection of Two-Level Supersaturated Designs for Main Effects Models”. Technometrics, vol. 65, no. 1, 2022, pp. 96-104, https://doi.org/10.1080/00401706.2022.2102080.
Singh, R., & Stufken, J. (2022). Selection of Two-Level Supersaturated Designs for Main Effects Models. Technometrics, 65(1), 96-104. https://doi.org/10.1080/00401706.2022.2102080
Singh, Rakhi, and John Stufken. “Selection of Two-Level Supersaturated Designs for Main Effects Models”. Technometrics 65, no. 1 (2022): 96-104. https://doi.org/10.1080/00401706.2022.2102080.
Singh R, Stufken J. Selection of Two-Level Supersaturated Designs for Main Effects Models. Technometrics. 2022;65(1):96-104.
Journal Categories
Science
Mathematics
Science
Mathematics
Probabilities
Mathematical statistics
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Title | 2013 | |||
| Title | 1999 | |||
| Title | 1997 | |||
| Strategies for Supersaturated Screening: Group Orthogonal and Constrained Var(s) Designs | Technometrics |
| 7 | 2020 |
| Searching for Powerful Supersaturated Designs | Journal of Quality Technology |
| 15 | 2015 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Factor selection in screening experiments by aggregation over random models | Computational Statistics & Data Analysis |
| 2024 | |
| Pareto-efficient designs for multi- and mixed-level supersaturated designs | Statistics and Computing |
| 2023 | |
| Best practices for multi- and mixed-level supersaturated designs | Journal of Quality Technology |
| 1 | 2023 |
Construction of Supersaturated Designs with Small Coherence for Variable Selection | The New England Journal of Statistics in Data Science | 2023 |
Citations Analysis
The category
Science: Mathematics: Probabilities. Mathematical statistics 3
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Pareto-efficient designs for multi- and mixed-level supersaturated designs and was published in 2023. The most recent citation comes from a 2024 study titled Factor selection in screening experiments by aggregation over random models. This article reached its peak citation in 2023, with 3 citations. It has been cited in 4 different journals. Among related journals, the Computational Statistics & Data Analysis cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.