Fully conditional specification in multivariate imputation
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Publication:3526396
DOI10.1080/10629360600810434zbMath1144.62332OpenAlexW2102252264WikidataQ59419613 ScholiaQ59419613MaRDI QIDQ3526396
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Publication date: 25 September 2008
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: http://dspace.library.uu.nl/handle/1874/19951
simulationmultiple imputationGibbs samplingproper imputationdistributional compatibilitymultivariate missing data
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Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Estimation for the multiple factor model when data are missing
- A proposal for handling missing data
- Conditionally specified distributions: An introduction. (With comments and a rejoinder).
- Inference from iterative simulation using multiple sequences
- Conditional specification of statistical models.
- Multiple Imputation After 18+ Years
- Compatible Conditional Distributions
- Inference and missing data
- A Multiple-Imputation Analysis of a Case-Control Study of the Risk of Primary Cardiac Arrest Among Pharmacologically Treated Hypertensives
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