Robust estimation for the covariance matrix of multi-variate time series

Title
Robust estimation for the covariance matrix of multi-variate time series
Author(s)
김병수이상열[이상열]
Keywords
HELLINGER DISTANCE ESTIMATION; MULTIVARIATE LOCATION; DISPERSION; DIVERGENCE; BEHAVIOR; MODELS
Issue Date
201109
Publisher
WILEY-BLACKWELL
Citation
JOURNAL OF TIME SERIES ANALYSIS, v.32, no.5, pp.469 - 481
Abstract
In this article, we study the robust estimation for the covariance matrix of stationary multi-variate time series. As a robust estimator, we propose to use a minimum density power divergence estimator (MDPDE) proposed by Basu et al. (1998). Particularly, the MDPDE is designed to perform properly when the time series is Gaussian. As a special case, we consider the robust estimator for the autocovariance function of univariate stationary time series. It is shown that the MDPDE is strongly consistent and asymptotically normal under regularity conditions. Simulation results are provided for illustration.
URI
http://hdl.handle.net/YU.REPOSITORY/24607http://dx.doi.org/10.1111/j.1467-9892.2010.00705.x
ISSN
0143-9782
Appears in Collections:
이과대학 > 통계학과 > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE