In this paper, the authors address the problem of the minimax estimator of linear combinations of stochastic regression coefficients and parameters in the general normal linear model with random effects. Under a quadr...In this paper, the authors address the problem of the minimax estimator of linear combinations of stochastic regression coefficients and parameters in the general normal linear model with random effects. Under a quadratic loss function, the minimax property of linear estimators is investigated. In the class of all estimators, the minimax estimator of estimable functions, which is unique with probability 1, is obtained under a multivariate normal distribution.展开更多
Under multivariant normal linear models, it is proved that usual loss estima-tors based on the uniformly minimum variance unbiased estimator and the best affinelyequivariant estimator of error variance are inadmissibl...Under multivariant normal linear models, it is proved that usual loss estima-tors based on the uniformly minimum variance unbiased estimator and the best affinelyequivariant estimator of error variance are inadmissible with squared error loss.展开更多
基金the National Natural Science Foundation of China(10271010)the Natural Science Foundation of Beijing(1032001)
文摘In this paper, the authors address the problem of the minimax estimator of linear combinations of stochastic regression coefficients and parameters in the general normal linear model with random effects. Under a quadratic loss function, the minimax property of linear estimators is investigated. In the class of all estimators, the minimax estimator of estimable functions, which is unique with probability 1, is obtained under a multivariate normal distribution.
基金Supported by National Natural Science Foundation of China.
文摘Under multivariant normal linear models, it is proved that usual loss estima-tors based on the uniformly minimum variance unbiased estimator and the best affinelyequivariant estimator of error variance are inadmissible with squared error loss.
基金Supported by Youth Science Foundation of Educational Committee of Jiangxi Province(GJJ12388)Natural Science Foundation of Jiangxi Province(20122BAB211007)National Social Science Foundation of China(12BTJ014)
