Regression With Missing X's: A Review |
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| 文献名称 | Regression With Missing X's: A Review | ||||||
| 文献作者 | Roderick J. A. Little | ||||||
| 作者所在单位 | Professor, Department of Biomathematics, UCLA School of Medicine, Los Angeles | ||||||
| 文献分类 | 已发表文献 | ||||||
| 学科一级分类 | 统计 | ||||||
| 学科二级分类 | 统计学 | ||||||
| 文献摘要 |
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 missing X's using only data on the X's are contrasted with likelihood- based methods that use data on the X's and Y. 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 naiveimputation methods. Bayesian simulation methods and multiple imputation are reviewed; these provide fruitful avenues for future research. |
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| 参考文献 |
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| 关键字 | Bayesian inference; Imputation; Incomplete data; Multiple imputation | ||||||
| 发表所在刊物（或来源） | Journal of the American Statistical Association, Vol. 87, No. 420 (Dec., 1992), pp. 1227-1237 | ||||||
| 发表时间 | Dec., 1992 | ||||||
| 适用研究领域 | |||||||
| 评论 | |||||||
| 上传时间 | 2011-1-20 13:38 | ||||||
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 油皮皮 |
油皮皮发表于:2011-4-22 23:42 写的非常好。下来看看。 |
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