• D-optimal minimax design crite...

Result: D-optimal minimax design criterion for two-level fractional factorial designs

Title:
D-optimal minimax design criterion for two-level fractional factorial designs
Authors:
WILMUT, Michael, JULIE ZHOU
Source:
Journal of statistical planning and inference. 141(1):576-587
Publisher Information:
Kidlington: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Combinatoire. Structures ordonnées, Combinatorics. Ordered structures, Combinatoire, Combinatorics, Plans d'expériences et configurations, Designs and configurations, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Généralités, General topics, Théorie de la décision, Decision theory, Plans d'expériences, Experimental design, Algorithme optimal, Optimal algorithm, Algoritmo óptimo, Algorithme recherche, Search algorithm, Algoritmo búsqueda, Borne inférieure, Lower bound, Cota inferior, Borne supérieure, Upper bound, Cota superior, Critère minimax, Minimax criterion, Criterio minimax, Décision statistique, Statistical decision, Decisión estadística, Estimation biaisée, Biased estimation, Estimación sesgada, Fonction perte, Loss function, Función pérdida, Modèle linéaire, Linear model, Modelo lineal, Méthode minimax, Minimax method, Método minimax, Méthode statistique, Statistical method, Método estadístico, Méthode séquentielle, Sequential method, Método secuencial, Performance algorithme, Algorithm performance, Resultado algoritmo, Plan expérience, Experimental design, Plan experiencia, Plan factoriel, Factorial design, Plan factorial, Plan optimal, Optimal design(statistics), Robustesse estimateur, Estimator robustness, Robustez estimador, Théorie décision, Decision theory, Teoría decisión, 05Bxx, 62C20, 62K05, 62K15, 62K25, 62K99, Fonction bornée, Modèle mal spécifié, Misspecification model, Plan D optimal, Optimal D design, Plan fractionnaire, Fractional design, Plan paramètre robuste, Plan recherche, Search design, Annealing algorithm, Model misspecification, Optimal design, Requirement set, Robust design, Sequential algorithm
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematics and Statistics, University of Victoria, Victoria, BC, V8W 3R4, Canada
ISSN:
0378-3758
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=23259344
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.23259344
Database:
PASCAL Archive

Further Information

A D-optimal minimax design criterion is proposed to construct two-level fractional factorial designs, which can be used to estimate a linear model with main effects and some specified interactions. D-optimal minimax designs are robust against model misspecification and have small biases if the linear model contains more interaction terms. When the D-optimal minimax criterion is compared with the D-optimal design criterion, we find that the D-optimal design criterion is quite robust against model misspecification. Lower and upper bounds derived for the loss functions of optimal designs can be used to estimate the efficiencies of any design and evaluate the effectiveness of a search algorithm. Four algorithms to search for optimal designs for any run size are discussed and compared through several examples. An annealing algorithm and a sequential algorithm are particularly effective to search for optimal designs.