Design of experiments for bivariate binary responses modelled by Copula functions

Denman, N. G., McGree, J. M., Eccleston, J. A. and Duffull, S. B. (2011) Design of experiments for bivariate binary responses modelled by Copula functions. Computational Statistics and Data Analysis, 55 4: 1509-1520. doi:10.1016/j.csda.2010.07.025

Author Denman, N. G.
McGree, J. M.
Eccleston, J. A.
Duffull, S. B.
Title Design of experiments for bivariate binary responses modelled by Copula functions
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
Publication date 2011-04-01
Sub-type Article (original research)
DOI 10.1016/j.csda.2010.07.025
Volume 55
Issue 4
Start page 1509
End page 1520
Total pages 12
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2012
Language eng
Abstract Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.
Keyword Bivariate binary response
Multiple responses
Multivariate distributions
Optimal design
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Issue - Section I: Computational Statistics

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2012 Collection
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Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 5 times in Scopus Article | Citations
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Created: Thu, 17 Mar 2011, 12:08:15 EST by Kay Mackie on behalf of School of Mathematics & Physics