Several Matrix Euclidean Norm Inequalities Involving Kantorovich Inequality

نویسندگان

  • Litong Wang
  • Hu Yang
چکیده

where λ1 ≥ · · · ≥ λn > 0 are the eigenvalues of A. It is a very useful tool to study the inefficiency of the ordinary least-squares estimate with one regressor in the linear model. Watson 1 introduced the ratio of the variance of the best linear unbiased estimator to the variance of the ordinary least-squares estimator. Such a lower bound of this ratio was provided by Kantorovich inequality 1.1 ; see, for example, 2, 3 . When regressors are more than one statisticians have to extend it. Marshall and Olkin 4 were the first to generalize Kantorovich inequality to matrices see, e.g., 5

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تاریخ انتشار 2009