Estimation of the covariance matrix with two-step monotone missing data
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
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.
High-dimensional estimation, Monotone missing data, 62H12, 62F12
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:kth:diva-185663DOI: 10.1080/03610926.2013.868085ISI: 000372828900006ScopusID: 2-s2.0-84961266780OAI: oai:DiVA.org:kth-185663DiVA: diva2:923410
FunderSwedish Research Council, 421-2008-1966
QC 201604262016-04-262016-04-252016-04-26Bibliographically approved