Forward selection error control in the analysis of supersaturated designs

Peter H. Westfall · S. Stanley Young · Dennis K. J. Lin

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Optimal Experimental Design Methods Statistical Methods in Clinical Trials Statistical Methods and Inference

Abstract

Abstract: Supersaturated designs are designed to assess the effects of many factors simultaneously. The assumption of “effect sparsity ” is often needed to justify the selection of these designs. However, when effect sparsity holds, Type I errors can easily occur. Forward-selection multiple test procedures are proposed to address and solve this problem. Key words and phrases: Adjusted p-values, control variates, multiplicity adjust-ment, resampling, variable selection. 1.

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References

1989Quick and Easy Analysis of Unreplicated Factorials 1986An Analysis for Unreplicated Fractional Factorials 1993A New Class of Supersaturated Designs 1992Analysis of Designed Experiments with Complex Aliasing 1962Some Systematic Supersaturated Designs 1993Construction of Supersaturated Designs Through Partially Aliased Interactions 1996An Algorithmic Approach to Constructing Supersaturated Designs 1995Generating Systematic Supersaturated Designs

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2017Disentangling good from bad practices in the selection of spatial or phylogenetic eigenvectors 2008Analysis of supersaturated designs via the Dantzig selector 2007Identifying spatial relationships at multiple scales: principal coordinates of neighbour matrices (PCNM) and geostatistical approaches 2002A Two-Stage Bayesian Model Selection Strategy for Supersaturated Designs 2010A comparison of design and model selection methods for supersaturated experiments 2009

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