In this paper, we consider simultaneous estimation of Poisson parameters in situations where we can use side information in aggregated data. We use standardized squared error and entropy loss functions. Bayesian shrinkage estimators are derived based on conjugate priors. We compare the risk functions of direct estimators and Bayesian estimators with respect to different priors that are constructed based on different subsets of observations. We obtain conditions for domination and also prove minimaxity and admissibility in a simple setting.
翻译:在本文中,我们考虑在可以使用侧边信息进行汇总数据的情况下同时估算 Poisson 参数。 我们使用标准化的平方误差和酶损耗功能。 贝叶斯缩缩缩估计值是根据共同前科得出的。 我们比较了直接测算员和贝叶斯测算员对基于不同观察子集构建的不同前科的风险功能。 我们获取了支配条件,并在简单环境下证明了微缩和可接受性。