2022-01-28T11:26:45Zhttps://repository.dl.itc.u-tokyo.ac.jp/oaioai:repository.dl.itc.u-tokyo.ac.jp:000426122021-03-02T02:12:29ZOptimal Ridge-type Estimators of Covariance Matrix in High DimensionKubokawa, Tatsuya98000Srivastrava, Muni S.98001Covariance matrixhigh dimensionnon-normal distributionnormal distributionridge-type estimatorrisk functionapplication/pdfThe problem of estimating the covariance matrix of normal and non-normal distributions is addressed when both the sample size and the dimension of covariance matrix tend to infinity. In this paper, we consider a class of ridge-type estimators which are linear combinations of the unbiased estimator and the identity matrix multiplied by a scalor statistic, and we derive a leading term of their risk functions relative to a quadratic loss function. Within this class, we obtain the optimal ridge-type estimator by minimizing the leading term in the risk approximation. It is interesting to note that the optimal weight is based on a statistic for testing sphericity of the covariance matrix.本文フィルはリンク先を参照のことtechnical report日本経済国際共同センター2013-10Discussion paper series. CIRJE-FCIRJE-F-906AA11450569enghttp://www.cirje.e.u-tokyo.ac.jp/research/dp/2013/2013cf906ab.htmlmetadata only access