Calibrated imputation for multivariate categorical data

Ton Waal () and Jacco Daalmans
Additional contact information
Ton Waal: Statistics Netherlands
Jacco Daalmans: Statistics Netherlands

AStA Advances in Statistical Analysis, 2024, vol. 108, issue 3, No 3, 545-576

Abstract: Abstract Non-response is a major problem for anyone collecting and processing data. A commonly used technique to deal with missing data is imputation, where missing values are estimated and filled in into the dataset. Imputation can become challenging if the variable to be imputed has to comply with a known total. Even more challenging is the case where several variables in the same dataset need to be imputed and, in addition to known totals, logical restrictions between variables have to be satisfied. In our paper, we develop an approach for a broad class of imputation methods for multivariate categorical data such that previously published totals are preserved while logical restrictions on the data are satisfied. The developed approach can be used in combination with any imputation model that estimates imputation probabilities, i.e. the probability that imputation of a certain category for a variable in a certain unit leads to the correct value for this variable and unit.

Keywords: Non-response; Edit rules; Fully conditional specification; Mass imputation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10182-023-00481-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:alstar:v:108:y:2024:i:3:d:10.1007_s10182-023-00481-z

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10182/PS2

DOI: 10.1007/s10182-023-00481-z

Access Statistics for this article

AStA Advances in Statistical Analysis is currently edited by Göran Kauermann and Yarema Okhrin

More articles in AStA Advances in Statistical Analysis from Springer, German Statistical Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().