MISSING DATA IMPUTATION USING THE MULTIVARIATE T-DISTRIBUTION
MISSING DATA IMPUTATION USING THE MULTIVARIATE T-DISTRIBUTION
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DOI：
10.1006/jmva.1995.1029
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发表时间：
1995-04-01
影响因子：
1.6
通讯作者：
LIU, C
中科院分区：
文献类型：
--
作者：
LIU, C
当矩形多元数据集包含缺失值时，使用多元t分布的缺失数据插补可能有用，特别是对于稳健的推断。一种有效的技术，称为单调数据增广算法，实现缺失数据填补使用多元t分布与已知和未知的权重，单调和非单调缺失数据，并与已知和未知的自由度。包括两个数值例子来说明的方法，使用多元t分布与使用正态分布得到的结果进行比较，并比较单调数据增强算法的收敛速度（矩形）数据增强算法的收敛速度。(C)出版社：Academic Press
When a rectangular multivariate data set contains missing values, missing data imputation using the multivariate t distribution appears potentially useful, especially for robust inferences. An efficient technique, called the monotone data augmentation algorithm, for implementing missing data imputation using the multivariate t distribution with known and unknown weights, with monotone and nonmonotone missing data, and with known and unknown degrees of freedom is presented. Two numerical examples are included to illustrate the methodology, to compare results obtained using the multivariate t distribution with results obtained using the normal distribution, and to compare the rate of convergence of the monotone data augmentation algorithm with the rate of convergence of the (rectangular) data augmentation algorithm. (C) 1995 Academic Press, Inc.