Design of experiments for bivariate binary responses modelled by Copula functions


Autoria(s): Denman, Nick; McGree, James; Eccleston, John; Duffull, Stephen
Data(s)

2011

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/42929/

Publicador

Elsevier BV

Relação

http://eprints.qut.edu.au/42929/1/42929.pdf

DOI:10.1016/j.csda.2010.07.025

Denman, Nick, McGree, James, Eccleston, John, & Duffull, Stephen (2011) Design of experiments for bivariate binary responses modelled by Copula functions. Computational Statistics and Data Analysis, 55(4), pp. 1509-1520.

Fonte

Faculty of Science and Technology; Mathematical Sciences

Palavras-Chave #010400 STATISTICS #080200 COMPUTATION THEORY AND MATHEMATICS #Bivariate binary response, Copulas, Dependence, D-optimality, Multiple responses, Multivariate distributions, Optimal design, P-optimality
Tipo

Journal Article