Recursive Estimation Procedures for Missing-Data Problems
本文研究了处理缺失数据的递归估计方法，推导了渐近性质，并针对随机缺失数据给出了易于使用的递归形式，通过双变量正态数据进行了数值比较，讨论了作为插补程序的应用。
Titterington (1984) proposes recursive methods for dealing with incomplete data. The present paper concentrates on versions of these for multiparameter problems involving missing data. Theorems are outlined from which asymptotic properties of the recursive procedures can be established and versions of the recursions, some of which are particularly easy to use, are written down for problems in which the missing data are missing at random. After illustration with exponential family models, the case of multivariate normal data is considered in detail. Numerical comparisons of the various methods are obtained using bivariate normal data. Application of the methods as imputation procedures is discussed.