Optimal Projective Three-Level Designs for Factor Screening and Interaction Detection

Autor:	Chien-Fu Wu, Shao-Wei Cheng, Hongquan Xu
Rok vydání:	2004
Předmět:	Statistics and Probability Set (abstract data type) Optimal design Mathematical optimization Applied Mathematics Modeling and Simulation Factor screening Orthogonal array Projective test Projection (set theory) Three level Mathematics
Zdroj:	Technometrics. 46:280-292
ISSN:	1537-2723 0040-1706
DOI:	10.1198/004017004000000310
Popis:	Orthogonal arrays (OAs) are widely used in industrial experiments for factor screening. Suppose that only a few of the factors in the experiments turn out to be important. An OA can be used not only for screening factors, but also for detecting interactions among a subset of active factors. In this article a set of optimality criteria is proposed to assess the performance of designs for factor screening, projection, and interaction detection, and a three-step approach is proposed to search for optimal designs. Combinatorial and algorithmic construction methods are proposed for generating new designs. Permutations of levels are used for improving the eligibility and estimation efficiency of the projected designs. The techniques are then applied to search for best three-level designs with 18 and 27 runs. Many new, efficient, and practically useful nonregular designs are found and their properties are discussed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8108c90668f130d36cb0ebe3ff949b35 https://doi.org/10.1198/004017004000000310 Zobrazit plný text záznamu