Pseudo-Bayesian D-optimal designs for longitudinal Poisson mixed models with correlated errors
Pseudo-Bayesian D-optimal designs for longitudinal Poisson mixed models with correlated errors
复制标题
具有相关误差的纵向泊松混合模型的伪贝叶斯 D 最优设计
DOI：
10.1007/s00180-018-0834-7
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发表时间：
--
影响因子：
1.3
通讯作者：
Yue Rong-Xian
中科院分区：
文献类型：
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作者：
Jiang Hong-Yan;Yue Rong-Xian
研究具有时间相关误差的纵向数据一阶泊松混合模型的拟贝叶斯d -最优设计问题。基于拟似然方法，得到了参数估计的标准近似协方差矩阵。此外，为了克服伪贝叶斯d -最优设计对先验均值选择的依赖，提出了一种基于未知参数分层先验分布的分层伪贝叶斯d -最优设计。结果表明，最优时间点数量取决于类间自回归系数和不同的成本约束。还讨论了等距设计与分层伪贝叶斯d -最优设计的相对效率。
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.