Condition-number-regularized covariance estimation

Estimation of high dimensional covariance matrices is known to be a difficult problem, has many applications and is of current interest to the larger statistics community. In many applications including the so-called 'large p, small n' setting, the estimate of the covariance matrix is requ...

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Bibliographische Detailangaben
Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2013-06, Vol.75 (3), p.427-450
Hauptverfasser:	Won, Joong-Ho, Lim, Johan, Kim, Seung-Jean, Rajaratnam, Bala
Format:	Artikel
Sprache:	eng
Schlagworte:	Condition number Conditioning Convex optimization Covariance Covariance estimation Covariance matrices Covariance matrix Cross-validation Decision theory Eigenvalue Eigenvalues Entropy Estimating techniques Estimation Estimators Investment risk Mathematical analysis Maximum likelihood estimation Multidimensional analysis Portfolio optimization Portfolio selection Probability Regularization Regularization methods Risk comparisons Samples Shrinkage Spectral theory Standard and Poors 500 Index Statistical analysis Statistical methods Statistics Studies Truncation
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