IMPROVEMENT OF AN UNBIASED ESTIMATOR OF THE INVERSE COVARIANCE MATRIX

DOI Web Site Open Access

Bibliographic Information

Other Title
  • 共分散行列の逆行列の不偏推定量の改良
  • キョウ ブンサン ギョウレツ ノ ギャクギョウレツ ノ フヘン スイテイリョウ ノ カイリョウ

Search this article

Abstract

In this paper, we consider the estimation of inverse of the covariance matrix. The estimation of the inverse of the covariance matrix under a multivariate normal distribution is an important issue in practical situations as well as from theoretical aspects. When the dimension is larger than the sample size, the Wishart matrix is singular, and thus many estimators have been constructed by using regularized estimation of the Wishart matrix. On the other hand, even if sample size is larger than dimension, it is well known that the usual estimator is typically not well-conditioned for the case dimension is large. In such situations, we propose the new estimators based on the unbiased estimator of the inverse of the covariance matrix. Also, the asymptotic optimalities with respect to loss for these estimators are obtained. Finally, the performances of our estimators are investigated by Monte Carlo simulations.

Journal

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top