Estimation of a Covariance Matrix under Stein's Loss
نویسندگان
چکیده
منابع مشابه
Optimal estimation of a large-dimensional covariance matrix under Stein's loss
This paper revisits the methodology of Stein (1975, 1986) for estimating a covariance matrix in the setting where the number of variables can be of the same magnitude as the sample size. Stein proposed to keep the eigenvectors of the sample covariance matrix but to shrink the eigenvalues. By minimizing an unbiased estimator of risk, Stein derived an ‘optimal’ shrinkage transformation. Unfortuna...
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We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even when the dimension of the vectors is smaller...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1985
ISSN: 0090-5364
DOI: 10.1214/aos/1176349756