Intrinsic covariance matrix estimation for multivariate elliptical distributions

Junhao Guo, Jie Zhou and Sanfeng Hu

Statistics & Probability Letters, 2020, vol. 162, issue C

Abstract: The property of statistical models not depending on the coordinate systems or model parametrization is one main interest of intrinsic inference in statistics. The intrinsic covariance matrix estimation is addressed for multivariate elliptical distributions in this paper. An optimal intrinsic covariance estimator is derived in the sense of minimizing the mean square Rao distance, and proved to own intrinsic unbiasedness. Specifically, the intrinsically unbiased estimators for elliptical distributions and mixture elliptical distributions are developed.

Keywords: Rao distance; Elliptical distributions; Covariance matrix estimation; Intrinsically unbiased estimation; Mixture of multivariate elliptical distributions (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715220300778
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:162:y:2020:i:c:s0167715220300778

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2020.108774

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().