IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2022i1p60-d1013447.html
   My bibliography  Save this article

A Method for Augmenting Supersaturated Designs with Newly Added Factors

Author

Listed:
  • Chun-Wei Zheng

    (State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China
    School of Statistics and Data Science, LPMC & KLMDASR, Nankai University, Tianjin 300071, China
    These authors contributed equally to this work.)

  • Zong-Feng Qi

    (State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China
    These authors contributed equally to this work.)

  • Qiao-Zhen Zhang

    (School of Statistics and Data Science, LPMC & KLMDASR, Nankai University, Tianjin 300071, China
    These authors contributed equally to this work.)

  • Min-Qian Liu

    (School of Statistics and Data Science, LPMC & KLMDASR, Nankai University, Tianjin 300071, China)

Abstract

Follow-up experimental designs are popularly used in industry. In practice, some important factors may be neglected for various reasons in the first-stage experiment and they need to be added in the next stage. In this paper, we propose a method for augmenting supersaturated designs with newly added factors and augmented levels using the Bayesian D -optimality criterion. In addition, we suggest using the integrated Bayesian D -optimal augmented design to plan the follow-up experiment when the newly added factors have been allowed to vary in an appropriate region. Examples and simulation results show that the augmented designs perform well in improving identified rates of latent factor effects.

Suggested Citation

  • Chun-Wei Zheng & Zong-Feng Qi & Qiao-Zhen Zhang & Min-Qian Liu, 2022. "A Method for Augmenting Supersaturated Designs with Newly Added Factors," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:60-:d:1013447
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/1/60/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/1/60/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gareth M. James & Peter Radchenko & Jinchi Lv, 2009. "DASSO: connections between the Dantzig selector and lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 127-142, January.
    2. Li, Runze & Lin, Dennis K. J., 2002. "Data analysis in supersaturated designs," Statistics & Probability Letters, Elsevier, vol. 59(2), pages 135-144, September.
    3. Jiaqi Liu & Zujun Ou & Liuping Hu & Kang Wang, 2019. "Lee discrepancy on mixed two- and three-level uniform augmented designs," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(10), pages 2409-2424, May.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Huang, Hengzhen & Yang, Jinyu & Liu, Min-Qian, 2014. "Functionally induced priors for componentwise Gibbs sampler in the analysis of supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 1-12.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. E. Androulakis & C. Koukouvinos, 2013. "A new variable selection method for uniform designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2564-2578, December.
    2. Das, Ujjwal & Gupta, Sudhir & Gupta, Shuva, 2014. "Screening active factors in supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 223-232.
    3. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    4. Gerda Claeskens, 2012. "Focused estimation and model averaging with penalization methods: an overview," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 272-287, August.
    5. Howard D. Bondell & Brian J. Reich, 2012. "Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1610-1624, December.
    6. Feng Li & Lu Lin & Yuxia Su, 2013. "Variable selection and parameter estimation for partially linear models via Dantzig selector," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(2), pages 225-238, February.
    7. Luigi Augugliaro & Angelo M. Mineo & Ernst C. Wit, 2013. "Differential geometric least angle regression: a differential geometric approach to sparse generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 471-498, June.
    8. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2013. "A Survey of L1 Regression," International Statistical Review, International Statistical Institute, vol. 81(3), pages 361-387, December.
    9. Brown Andrew Anand & Richardson Sylvia & Whittaker John, 2011. "Application of the Lasso to Expression Quantitative Trait Loci Mapping," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-35, March.
    10. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    11. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    12. Emmanouil Androulakis & Christos Koukouvinos & Kalliopi Mylona & Filia Vonta, 2010. "A real survival analysis application via variable selection methods for Cox's proportional hazards model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1399-1406.
    13. Li, Peng & Zhao, Shengli & Zhang, Runchu, 2010. "A cluster analysis selection strategy for supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1605-1612, June.
    14. Singh, Rakhi & Stufken, John, 2024. "Factor selection in screening experiments by aggregation over random models," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    15. Koki Momoki & Takuma Yoshida, 2024. "Hypothesis testing for varying coefficient models in tail index regression," Statistical Papers, Springer, vol. 65(6), pages 3821-3852, August.
    16. Lili Pan & Ziyan Luo & Naihua Xiu, 2017. "Restricted Robinson Constraint Qualification and Optimality for Cardinality-Constrained Cone Programming," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 104-118, October.
    17. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    18. Gerhard Tutz & Moritz Berger, 2018. "Tree-structured modelling of categorical predictors in generalized additive regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 737-758, September.
    19. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    20. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:60-:d:1013447. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.