FORWARD SELECTION ERROR CONTROL IN THE ANALYSIS OF SUPERSATURATED DESIGNS

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FORWARD SELECTION ERROR CONTROL IN THE ANALYSIS OF SUPERSATURATED DESIGNS

Peter H. Westfall,S. Stanley Young,Dennis K. J. Lin-1998-01-01

97

TL;DRAbstract

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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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.

Keywords

Selection (genetic algorithm)Computer scienceControl (management)StatisticsMathematicsArtificial intelligence

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