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Estimation of the covariance matrix with two-step monotone missing data
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics. Tokyo Univ Sci, Fac Sci, Dept Math Informat Sci, Japan.
2016 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 45, no 7, 1910-1922 p.Article in journal (Refereed) PublishedText
Abstract [en]

We suggest shrinkage based technique for estimating covariance matrix in the high-dimensional normal model with missing data. Our approach is based on the monotone missing scheme assumption, meaning that missing values patterns occur completely at random. Our asymptotic framework allows the dimensionality p grow to infinity together with the sample size, N, and extends the methodology of Ledoit and Wolf (2004) to the case of two-step monotone missing data. Two new shrinkage-type estimators are derived and their dominance properties over the Ledoit and Wolf (2004) estimator are shown under the expected quadratic loss. We perform a simulation study and conclude that the proposed estimators are successful for a range of missing data scenarios.

Place, publisher, year, edition, pages
2016. Vol. 45, no 7, 1910-1922 p.
Keyword [en]
High-dimensional estimation, Monotone missing data, 62H12, 62F12
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-185663DOI: 10.1080/03610926.2013.868085ISI: 000372828900006ScopusID: 2-s2.0-84961266780OAI: diva2:923410
Swedish Research Council, 421-2008-1966

QC 20160426

Available from: 2016-04-26 Created: 2016-04-25 Last updated: 2016-04-26Bibliographically approved

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