Regression with MissingX's: A Review
作者:
RoderickJ. A. Little,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1992)
卷期:
Volume 87,
issue 420
页码: 1227-1237
ISSN:0162-1459
年代: 1992
DOI:10.1080/01621459.1992.10476282
出版商: Taylor & Francis Group
关键词: Bayesian inference;Imputation;Incomplete data;Multiple imputation
数据来源: Taylor
摘要:
The literature of regression analysis with missing values of the independent variables is reviewed. Six classes of procedures are distinguished: complete case analysis, available case methods, least squares on imputed data, maximum likelihood, Bayesian methods, and multiple imputation. Methods are compared and illustrated when missing data are confined to one independent variable, and extensions to more general patterns are indicated. Attention is paid to the performance of methods when the missing data are not missing completely at random. Least squares methods that fill in missingX's using only data on theX's are contrasted with likelihood-based methods that use data on theX's andY. The latter approach is preferred and provides methods for elaboration of the basic normal linear regression model. It is suggested that more widely distributed software is needed that advances beyond complete-case analysis, available-case analysis, and naive imputation methods. Bayesian simulation methods and multiple imputation are reviewed; these provide fruitful avenues for future research.
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