期刊名称：Electronic Journal of Applied Statistical Analysis
电子版ISSN：2070-5948
出版年度：2016
卷号：9
期号：1
页码：122-133
语种：English
出版社：University of Salento
摘要：Missing observations in dependent variable is a common feature in survey research. A number of techniques have been developed to impute missing data. In this article, we have evaluated the performance of several impu- tation methods namely mean-before method, mean-before-after method and expectation-maximization algorithm in linear structural relationship model. On the basis of mean absolute error and root mean square error for both simulated and real data sets, we have shown that expectation-maximization algorithm is the most effective method than the other two imputation meth- ods to analyze the missing data in linear structural relationship model.
关键词：Errors-in-variable model;Imputation method;EM-algorithm;Performance indicator;Demographic health survey
