Combining κNN imputation and bootstrap calibrated empirical likelihood for incomplete data analysis

Qin, Y; Zhang, S; Zhang, C

Combining κNN imputation and bootstrap calibrated empirical likelihood for incomplete data analysis

Qin, Y Zhang, S Zhang, C

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Publication Type:: Journal Article
Citation:: International Journal of Data Warehousing and Mining, 2010, 6 (4), pp. 61 - 73
Issue Date:: 2010-10-01

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Field	Value	Language
dc.contributor.author	Qin, Y	en_US
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2010-10-01	en_US
dc.identifier.citation	International Journal of Data Warehousing and Mining, 2010, 6 (4), pp. 61 - 73	en_US
dc.identifier.issn	1548-3924	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13475
dc.description.abstract	The κ-nearest neighbor (κNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This paper studies a new estimation for missing (or incomplete) data that is a combination of the κNN imputation and bootstrap calibrated EL (Empirical Likelihood). The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the κNN imputation, but also inherits the advantages of the EL method compared to the normal approximation method. Simulation results demonstrate that the bootstrap calibrated EL method performs quite well in estimating confidence intervals for the imputed data with κNN imputation method. Copyright © 2010.	en_US
dc.relation.ispartof	International Journal of Data Warehousing and Mining	en_US
dc.relation.isbasedon	10.4018/jdwm.2010100104	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Combining κNN imputation and bootstrap calibrated empirical likelihood for incomplete data analysis	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	6	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0804 Data Format	en_US
dc.location.activity	ISI:000283607700004	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

The κ-nearest neighbor (κNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This paper studies a new estimation for missing (or incomplete) data that is a combination of the κNN imputation and bootstrap calibrated EL (Empirical Likelihood). The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the κNN imputation, but also inherits the advantages of the EL method compared to the normal approximation method. Simulation results demonstrate that the bootstrap calibrated EL method performs quite well in estimating confidence intervals for the imputed data with κNN imputation method. Copyright © 2010.

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http://hdl.handle.net/10453/13475