TY - JOUR
T1 - Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices
AU - Wang, Cheng
AU - Tong, Tiejun
AU - Cao, Longbing
AU - Miao, Baiqi
PY - 2014/3
Y1 - 2014/3
N2 - 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.
AB - 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.
KW - High-dimensional data
KW - Shrinkage estimator
KW - Large p small n
KW - U-statistic
UR - http://www.scopus.com/inward/record.url?scp=84892976068&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DP1096218
UR - http://purl.org/au-research/grants/arc/LP100200774
U2 - 10.1016/j.jmva.2013.12.012
DO - 10.1016/j.jmva.2013.12.012
M3 - Article
SN - 0047-259X
VL - 125
SP - 222
EP - 232
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
ER -