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Robust estimation for the covariance matrix of multi‐variate time series
Author(s) -
Kim Byungsoo,
Lee Sangyeol
Publication year - 2011
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2010.00705.x
Subject(s) - autocovariance , mathematics , estimator , univariate , series (stratigraphy) , covariance matrix , gaussian , random variate , estimation of covariance matrices , covariance function , covariance , statistics , random variable , multivariate statistics , mathematical analysis , paleontology , biology , physics , fourier transform , quantum mechanics
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.