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A Class of Three-Level Designs for Definitive Screening in the Presence of Second-Order Effects
TLDR
We propose a new class of designs that have three levels, provide estimates of main effects that are unbiased by any second-order effect, require only one more than twice as many runs as there are factors. Expand
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A simple Bayesian modification of D-optimal designs to reduce dependence on an assumed model
D-optimal and other computer-generated experimental designs have been criticized for being too dependent on an assumed statistical model. To address this criticism, we introduce the notion ofExpand
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Split-Plot Designs: What, Why, and How
The past decade has seen rapid advances in the development of new methods for the design and analysis of split-plot experiments. Unfortunately, the value of these designs for industrialExpand
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Bayesian D-optimal supersaturated designs
We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size,Expand
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Optimal Supersaturated Designs
We consider screening experiments where an investigator wishes to study many factors using fewer observations. Our focus is on experiments with two-level factors and a main effects model withExpand
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Model discrimination—another perspective on model-robust designs
Recent progress in model-robust designs has focused on maximizing estimation capacities. However, for a given design, two competing models may be both estimable and yet difficult or impossible toExpand
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Alternatives to resolution IV screening designs in 16 runs
The resolution IV regular fractional factorial designs in 16 runs for six, seven, and eight factors are in standard use. They are economical and provide clear estimates of main effects whenExpand
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Definitive Screening Designs with Added Two-Level Categorical Factors*
TLDR
We develop column-augmented DSDs that can accommodate any number of two-level qualitative factors using two methods, and show that they are still definitive in the sense that the estimates of all main effects continue to be unbiased by any active second-order effects. Expand
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Model-robust supersaturated and partially supersaturated designs
Abstract Supersaturated designs are an increasingly popular tool for screening factors in the presence of effect sparsity. The advantage of this class of designs over resolution III factorial designsExpand
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JMP statistical discovery software
TLDR
JMP is a statistical software environment that enables scientists, engineers, and business analysts to make discoveries through data exploration. Expand
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