Optimal design of large-scale screening experiments: a critical look at the coordinate-exchange algorithm
| Autor: | Kenneth Sörensen, Daniel Palhazi Cuervo, Peter Goos |
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| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Optimal design Mathematical optimization Scale (ratio) Economics Iterated local search Coordinate-exchange algorithm Metaheuristic 01 natural sciences Measure (mathematics) Theoretical Computer Science 010104 statistics & probability Orthogonality 0502 economics and business 0101 mathematics Selection (genetic algorithm) Mathematics Computer. Automation 050210 logistics & transportation Design of experiments 05 social sciences D-optimality criterion Optimal design of experiments Computational Theory and Mathematics Statistics Probability and Uncertainty Algorithm |
| Zdroj: | Statistics and computing |
| ISSN: | 1573-1375 0960-3174 |
| DOI: | 10.1007/s11222-014-9467-z |
| Popis: | © 2014, Springer Science+Business Media New York. We focus on the D-optimal design of screening experiments involving main-effects regression models, especially with large numbers of factors and observations. We propose a new selection strategy for the coordinate-exchange algorithm based on an orthogonality measure of the design. Computational experiments show that this strategy finds better designs within an execution time that is 30 % shorter than other strategies. We also provide strong evidence that the use of the prediction variance as a selection strategy does not provide any added value in comparison to simpler selection strategies. Additionally, we propose a new iterated local search algorithm for the construction of D-optimal experimental designs. This new algorithm outperforms the original coordinate-exchange algorithm. ispartof: Statistics and Computing vol:26 issue:1 pages:15-28 status: published |
| Databáze: | OpenAIRE |
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