Synthetic Multiple-Imputation Procedure for Multistage Complex Samples

Hanzhi Zhou, Michael R. Elliott and Trivellore E. Raghunathan

Mathematica Policy Research Reports from Mathematica Policy Research

Abstract: Multiple imputation (MI) is commonly used when item-level missing data are present. However, MI requires that survey design information be built into the imputation models.

Keywords: Finite population Bayesian bootstrap; Haldane prior; stratified sample; clustered sample; sample weights (search for similar items in EconPapers)
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.degruyter.com/view/j/jos.2016.32.issue- ... 11/jos-2016-0011.xml (text/html)
Our link check indicates that this URL is bad, the error code is: 410 Gone (http://www.degruyter.com/view/j/jos.2016.32.issue-1/jos-2016-0011/jos-2016-0011.xml [301 Moved Permanently]--> https://www.degruyterbrill.com:443/view/j/jos.2016.32.issue-1/jos-2016-0011/jos-2016-0011.xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mpr:mprres:f8c30b6f4346469f9880fdb2f63171f4

Access Statistics for this paper

More papers in Mathematica Policy Research Reports from Mathematica Policy Research Mathematica Policy Research P.O. Box 2393 Princeton, NJ 08543-2393 Attn: Communications. Contact information at EDIRC.
Bibliographic data for series maintained by Joanne Pfleiderer () and Cindy George ( this e-mail address is bad, please contact ).