Treffer: Evaluating a sequential tree-based procedure for multivariate imputation of complex missing data structures

Title:
Evaluating a sequential tree-based procedure for multivariate imputation of complex missing data structures
Source:
Quality & quantity. 47(4):1991-2008
Publisher Information:
Dordrecht: Springer, 2013.
Publication Year:
2013
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Geographic:
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics, University of Milano-Bicocca, Milan, Italy
Division of Social Statistics, Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, United Kingdom
ISSN:
0033-5177
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Sociology

FRANCIS
Accession Number:
edscal.27746690
Database:
PASCAL Archive

Weitere Informationen

Item nonresponse in survey data can pose significant problems for social scientists carrying out statistical modeling using a large number of explanatory variables. A number of imputation methods exist but many only deal with univariate imputation, or relatively simple cases of multivariate imputation, often assuming a monotone pattern of missingness. In this paper we evaluate a tree-based approach for multivariate imputation using real data from the 1970 British Cohort Study, known for its complex pattern of nonresponse. The performance of this tree-based approach is compared to mode imputation and a sequential regression based approach within a simulation study.