Non-Bayesian mulitple imputation
Journal article, Peer reviewed
MetadataShow full item record
Original versionJournal of Official Statistics, Vol. 23, No.4 (2007), 433-491
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 article 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
Vedlagt Discussion og og forfatterens rejoinder. With permission from Statistics Sweden. The original publication is available at http://www.jos.nu/Contents/jos_online.asp Artikkelen er også utgitt i Statistisk sentralbyrås serie Særtrykk/Reprints nr 323.