Hierarchical Poisson Regression Modeling
提出一种分层泊松回归模型，使用非可交换伽马分布处理个体参数，通过无信息先验避免方差分量最大似然估计的异常行为，模拟显示该方法在覆盖概率和风险上优于其他方法，并提供快速密度近似和公开计算程序。
Abstract The Poisson model and analyses here feature nonexchangeable gamma distributions (although exchangeable following a scale transformation) for individual parameters, with standard deviations proportional to means. A relatively uninformative prior distribution for the shrinkage values eliminates the ill behavior of maximum likelihood estimators of the variance components. When tested in simulation studies, the resulting procedure provides better coverage probabilities and smaller risk than several other published rules, and thus works well from Bayesian and frequentist perspectives alike. The computations provide fast, accurate density approximations to individual parameters and to structural regression coefficients. The computer program is publicly available through Statlib.