Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices

Wang, C; Tong, T; Cao, L; Miao, B

Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices

Wang, C Tong, T Cao, L

Miao, B

Permalink

Publisher:: Academic Press
Publication Type:: Journal Article
Citation:: Journal of Multivariate Analysis, 2014, 125, pp. 222-232
Issue Date:: 2014-01

Closed Access

	Filename	Description	Size
	JMA-non-parametric shrinkage.pdf	Published version	483.81 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, C
dc.contributor.author	Tong, T
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.contributor.author	Miao, B
dc.date.accessioned	2022-01-19T10:02:11Z
dc.date.available	2022-01-19T10:02:11Z
dc.date.issued	2014-01
dc.identifier.citation	Journal of Multivariate Analysis, 2014, 125, pp. 222-232
dc.identifier.issn	0047-259X
dc.identifier.issn	1095-7243
dc.identifier.uri	http://hdl.handle.net/10453/153373
dc.description.abstract	In this paper, a shrinkage estimator for the population mean is proposed under known quadratic loss functions with unknown covariance matrices. The new estimator is non-parametric in the sense that it does not assume a specific parametric distribution for the data and it does not require the prior information on the population covariance matrix. Analytical results on the improvement of the proposed shrinkage estimator are provided and some corresponding asymptotic properties are also derived. Finally, we demonstrate the practical improvement of the proposed method over existing methods through extensive simulation studies and real data analysis.
dc.language	English
dc.publisher	Academic Press
dc.relation	http://purl.org/au-research/grants/arc/DP1096218
dc.relation	http://purl.org/au-research/grants/arc/LP100200774
dc.relation.ispartof	Journal of Multivariate Analysis
dc.relation.isbasedon	10.1016/j.jmva.2013.12.012
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics, 1403 Econometrics
dc.subject.classification	Statistics & Probability
dc.title	Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices
dc.type	Journal Article
utslib.citation.volume	125
utslib.for	0104 Statistics
utslib.for	0104 Statistics
utslib.for	1403 Econometrics
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/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2022-01-19T10:02:10Z
pubs.publication-status	Published
pubs.volume	125

Abstract:

In this paper, a shrinkage estimator for the population mean is proposed under known quadratic loss functions with unknown covariance matrices. The new estimator is non-parametric in the sense that it does not assume a specific parametric distribution for the data and it does not require the prior information on the population covariance matrix. Analytical results on the improvement of the proposed shrinkage estimator are provided and some corresponding asymptotic properties are also derived. Finally, we demonstrate the practical improvement of the proposed method over existing methods through extensive simulation studies and real data analysis.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/153373