Design of Experiments for Screening

TL;DR

This paper reviews various design methods for screening experiments, discussing their strengths and weaknesses, and compares six screening techniques through a novel empirical study on common examples.

Contribution

It provides a comprehensive review of screening experiment designs and introduces a comparative analysis of six methods using real exemplars.

Findings

Six screening methods are empirically compared.

Different designs show varying effectiveness depending on the context.

The study highlights strengths and limitations of each screening approach.

Abstract

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared.