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EconStor
Research . 2005
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Non-Bayesian multiple imputation

Authors: Jan F. Bjørnstad;

Non-Bayesian multiple imputation

Abstract

Abstract: Multiple imputation is a method specifically designed for variance estimation in the presence of missing data. Rubin’s combination formula requires that the imputation method is “proper” which essentially means that the imputations are random draws from a posterior distribution in a Bayesian framework. In national statistical institutes (NSI’s) like Statistics Norway, the methods used for imputing for nonresponse are typically non-Bayesian, e.g., some kind of stratified hot-deck. Hence, Rubin’s method of multiple imputation is not valid and cannot be applied in NSI’s. This paper deals with the problem of deriving an alternative combination formula that can be applied for imputation methods typically used in NSI’s and suggests an approach for studying this problem. Alternative combination formulas are derived for certain response mechanisms and hot-deck type imputation methods. Keywords: Multiple imputation, survey sampling, nonresponse, hot-deck imputation

Country
Norway
Related Organizations
Keywords

JEL classification: C15, JEL classification: C13, VDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412, ddc:330, Survey sampling, nonresponse, hot-deck imputation, Multiple imputation, C13, C15, Multiple imputation; survey sampling; nonresponse; hot-deck imputation, C42, survey sampling, Nonresponse, JEL classification: C42, jel: jel:C42, jel: jel:C13, jel: jel:C15

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green