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Computational Statistics

, Volume 34, Issue 1, pp 71–87 | Cite as

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

  • Hong-Yan Jiang
  • Rong-Xian YueEmail author
Original Paper
First Online:
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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 
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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Mathematics and ScienceShanghai Normal UniversityShanghaiChina
  2. 2.Department of Mathematics and PhysicsHuaiyin Institute of TechnologyHuaianChina
  3. 3.Scientific Computing Key Laboratory of Shanghai UniversitiesShanghaiChina

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