MAXIMUM LIKELIHOOD FROM INCOMPLETE DATA VIA EM ALGORITHM
MAXIMUM LIKELIHOOD FROM INCOMPLETE DATA VIA EM ALGORITHM
复制标题
DOI：
10.1111/j.2517-6161.1977.tb01600.x
复制
发表时间：
1977-01-01
期刊：
影响因子：
--
通讯作者：
RUBIN, DB
中科院分区：
文献类型：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
总结一个广泛适用的算法计算最大似然估计从不完全的数据是在不同程度的一般性。理论显示的单调行为的可能性和算法的收敛性。许多例子勾勒，包括缺失值的情况下，应用分组，删失或截断数据，有限混合模型，方差分量估计，超参数估计，迭代加权最小二乘和因子分析。
SummaryA 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 squares and factor analysis.