日期精度：到日 1 篇 · 到月 1 篇 · 到年 1 篇
二维列联表的经验贝叶斯方法
An empirical Bayes method for smoothing two-way tables is presented. It is based on the use of the log linear model and a normal prior distribution. Estimation of the variance component in the prior is discussed, and two approximations are considered, one which utilizes the EM algorithm.
二维列联表的经验贝叶斯方法
An empirical Bayes method for smoothing two-way tables is presented. It is based on the use of the log linear model and a normal prior distribution. Estimation of the variance component in the prior is discussed, and two approximations are considered, one which utilizes the EM algorithm.
通过 EM 算法对不完整数据进行最大似然估计
Summary A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least…