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Modelling by supersaturated designs

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  • Georgiou, Stelios D.

Abstract

The analysis of supersaturated designs is an interesting problem of great importance since it provides economic estimates. Moreover, this problem is challenging due to the fact that the design matrix has a complicated structure. The identification of the active factors in supersaturated designs is investigated. The singular value decomposition (SVD), principal components analysis and regression analysis are used together in an SVD principal regression method to reveal the hidden true linear model. Special cases are studied by using simulation data under idealized conditions. Simulations are used to investigate the performance of the method and also to compare the proposed method with other known methods from the literature.

Suggested Citation

  • Georgiou, Stelios D., 2008. "Modelling by supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 428-435, December.
    • Handle: RePEc:eee:csdana:v:53:y:2008:i:2:p:428-435
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    References listed on IDEAS

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    1. Li, Runze & Lin, Dennis K. J., 2002. "Data analysis in supersaturated designs," Statistics & Probability Letters, Elsevier, vol. 59(2), pages 135-144, September.
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    Cited by:

    1. 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.
    2. Stamatis Choudalakis & Marilena Mitrouli & Athanasios Polychronou & Paraskevi Roupa, 2021. "Solving High-Dimensional Problems in Statistical Modelling: A Comparative Study," Mathematics, MDPI, vol. 9(15), pages 1-16, July.
    3. Gutman, Alex J. & White, Edward D. & Lin, Dennis K.J. & Hill, Raymond R., 2014. "Augmenting supersaturated designs with Bayesian D-optimality," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1147-1158.

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