Pseudo-Bayesian D-optimal designs for longitudinal Poisson mixed models with correlated errors

Hong-Yan Jiang () and Rong-Xian Yue ()
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Hong-Yan Jiang: Shanghai Normal University
Rong-Xian Yue: Shanghai Normal University

Computational Statistics, 2019, vol. 34, issue 1, No 4, 87 pages

Abstract: Abstract This paper is concerned with the problem of pseudo-Bayesian D-optimal designs for the first-order Poisson mixed model for longitudinal data with time-dependent correlated errors. A standard approximate covariance matrix of the parameter estimation is obtained based on the quasi-likelihood method. Furthermore, to overcome the dependence of pseudo-Bayesian D-optimal designs on the choice of the prior mean, a hierarchical pseudo-Bayesian D-optimal designs based on the hierarchical prior distribution of unknown parameters is proposed. The results show that the optimal number of time points depends on both the interclass autoregressive coefficients and different cost constraints. The relative efficiency of equidistant designs compared with the hierarchical pseudo-Bayesian D-optimal designs is also discussed.

Keywords: Quasi-likelihood method; Estimating equation; Repeated measures; Hierarchical design; Longitudinal data (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00180-018-0834-7

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