A Simulated Annealing Algorithm for D-Optimal Design for 2-Way and 3-Way Polynomial Regression with Correlated Observations
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-26
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Much of the previous work in D-optimal design for regression models with correlated errors focused on polynomial models with a single predictor variable, in large part because of the intractability of an analytic solution.
In this paper, we present a modified, improved simulated annealing algorithm, providing practical approaches to specifications of the annealing cooling parameters, thresholds, and search neighborhoods for the perturbation scheme, which finds approximate D-optimal designs for 2-way and 3-way polynomial regression for a variety of specific correlation structures with a given correlation coefficient.
Results in each correlated-errors case are compared with traditional simulated annealing algorithm, that is, the SA algorithm without our improvement.
Our improved simulated annealing results had generally higher D-efficiency than traditional simulated annealing algorithm, especially when the correlation parameter was well away from 0.
American Psychological Association (APA)
Li, Chang& Coster, Daniel C.. 2014. A Simulated Annealing Algorithm for D-Optimal Design for 2-Way and 3-Way Polynomial Regression with Correlated Observations. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-495458
Modern Language Association (MLA)
Li, Chang& Coster, Daniel C.. A Simulated Annealing Algorithm for D-Optimal Design for 2-Way and 3-Way Polynomial Regression with Correlated Observations. Journal of Applied Mathematics No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-495458
American Medical Association (AMA)
Li, Chang& Coster, Daniel C.. A Simulated Annealing Algorithm for D-Optimal Design for 2-Way and 3-Way Polynomial Regression with Correlated Observations. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-495458
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-495458