Missing data analysis: A kernel-based multi-imputation approach

Zhang, S; Jin, Z; Zhu, X; Zhang, J

Missing data analysis: A kernel-based multi-imputation approach

Zhang, S Jin, Z Zhu, X Zhang, J

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Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5300 LNCS pp. 122 - 142
Issue Date:: 2009-07-20

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Jin, Z	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Zhang, J	en_US
dc.date.issued	2009-07-20	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5300 LNCS pp. 122 - 142	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9044
dc.description.abstract	Many missing data analysis techniques are of single-imputation. However, single-imputation cannot provide valid standard errors and confidence intervals, since it ignores the uncertainty implicit in the fact that the imputed values are not the actual values. Filling in each missing value with a set of plausible values is called multi-imputation. In this paper we propose a kernel- based stochastic non-parametric multi-imputation method under MAR (Missing at Random) and MCAR (Missing Completely at Random) missing mechanisms in nonparametric regression settings. Furthermore, we present a kernel-based stochastic semi-parametric multi-imputation method while we have some priori knowledge about the dataset with missing. Our algorithms are designed specifically with the aim of optimizing the confidence-interval and the relative efficiency. The proposed technique is evaluated by experimentations, using simulation data and real data, and the results demonstrate that our method performs much better than the NORM method, and is promising. © Springer-Verlag Berlin Heidelberg 2009.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0985456
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-00212-0_7	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Missing data analysis: A kernel-based multi-imputation approach	en_US
dc.type	Journal Article
utslib.citation.volume	5300 LNCS	en_US
utslib.for	0807 Library and Information Studies	en_US
utslib.for	080604 Database Management	en_US
utslib.for	080611 Information Systems Theory	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	5300 LNCS	en_US

Abstract:

Many missing data analysis techniques are of single-imputation. However, single-imputation cannot provide valid standard errors and confidence intervals, since it ignores the uncertainty implicit in the fact that the imputed values are not the actual values. Filling in each missing value with a set of plausible values is called multi-imputation. In this paper we propose a kernel- based stochastic non-parametric multi-imputation method under MAR (Missing at Random) and MCAR (Missing Completely at Random) missing mechanisms in nonparametric regression settings. Furthermore, we present a kernel-based stochastic semi-parametric multi-imputation method while we have some priori knowledge about the dataset with missing. Our algorithms are designed specifically with the aim of optimizing the confidence-interval and the relative efficiency. The proposed technique is evaluated by experimentations, using simulation data and real data, and the results demonstrate that our method performs much better than the NORM method, and is promising. © Springer-Verlag Berlin Heidelberg 2009.

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http://hdl.handle.net/10453/9044