Missing Data, Imputation, and the Bootstrap
作者:
Bradley Efron,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1994)
卷期:
Volume 89,
issue 426
页码: 463-475
ISSN:0162-1459
年代: 1994
DOI:10.1080/01621459.1994.10476768
出版商: Taylor & Francis Group
关键词: Bayesian bootstrap;Bootstrap confidence intervals;Data augmentation;Ignorable nonresponse;Nonparametric MLE
数据来源: Taylor
摘要:
Missing data refers to a class of problems made difficult by the absence of some portions of a familiar data structure. For example, a regression problem might have some missing values in the predictor vectors. This article concerns nonparametric approaches to assessing the accuracy of an estimator in a missing data situation. Three main topics are discussed: bootstrap methods for missing data, these methods' relationship to the theory of multiple imputation, and computationally efficient ways of executing them. The simplest form of nonparametric bootstrap confidence interval turns out to give convenient and accurate answers. There are interesting practical and theoretical differences between bootstrap methods and the multiple imputation approach, as well as some useful similarities.
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