The necessary and sufficient conditions for a linear estimator of a linear estimable functionof regression coefficients in a general fixed effects linear model with the assumptions of normality to beadmissible in the ...The necessary and sufficient conditions for a linear estimator of a linear estimable functionof regression coefficients in a general fixed effects linear model with the assumptions of normality to beadmissible in the class of all estimators under matrix liss function are given.For a general randomeffects or mixted effects linear model the necessary and sufficient conditions are obtained too.展开更多
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
基金The work is supported by the Science Fund R850020 of the Academia Sinica
文摘The necessary and sufficient conditions for a linear estimator of a linear estimable functionof regression coefficients in a general fixed effects linear model with the assumptions of normality to beadmissible in the class of all estimators under matrix liss function are given.For a general randomeffects or mixted effects linear model the necessary and sufficient conditions are obtained too.
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.
