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

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. Fu...

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Bibliographic Details
Published in:Computational statistics 2019-03, Vol.34 (1), p.71-87
Main Authors: Jiang, Hong-Yan, Yue, Rong-Xian
Format: Article
Language:English
Subjects:
Bayesian analysis
Covariance matrix
Economic Theory/Quantitative Economics/Mathematical Methods
Mathematical models
Mathematics and Statistics
Original Paper
Parameter estimation
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Statistics
Time dependence
Within-subjects design
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Summary: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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-018-0834-7