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Multiple Imputation for Incomplete Data With Semicontinuous Variables
- Source :
- Journal of the American Statistical Association. 98:703-715
- Publication Year :
- 2003
- Publisher :
- Informa UK Limited, 2003.
-
Abstract
- We consider the application of multiple imputation to data containing not only partially missing categorical and continuous variables, but also partially missing 'semicontinuous' variables (variables that take on a single discrete value with positive probability but are otherwise continuously distributed). As an imputation model for data sets of this type, we introduce an extension of the standard general location model proposed by Olkin and Tate; our extension, the blocked general location model, provides a robust and general strategy for handling partially observed semicontinuous variables. In particular, we incorporate a two-level model for the semicontinuous variables into the general location model. The first level models the probability that the semicontinuous variable takes on its point mass value, and the second level models the distribution of the variable given that it is not at its point mass. In addition, we introduce EM and data augmentation algorithms for the blocked general location model w...
- Subjects :
- Statistics and Probability
Location model
Complete information
Statistics
Expectation–maximization algorithm
Probability distribution
Applied mathematics
Imputation (statistics)
Statistics, Probability and Uncertainty
Missing data
Categorical variable
Statistical hypothesis testing
Mathematics
Subjects
Details
- ISSN :
- 1537274X and 01621459
- Volume :
- 98
- Database :
- OpenAIRE
- Journal :
- Journal of the American Statistical Association
- Accession number :
- edsair.doi.dedup.....848375d7998f5020dd771521bac4a793