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协方差矩阵奇异的一般线性模型中的精确线性参数约束AI 解读

An attempt is made to develop a best linear unbiased estimator of β in the model y = Xβ + u, with known singular covariance matrix V of u and restrictions on β. Two operational criteria for optimality are considered: minimum expected quadratic loss and minimum generalized variance. It is shown that these criteria lead to the same estimator, the well-known least-squares estimator, as developed by Theil (1971).

奇异协方差一般线性模型中的参数精确线性约束AI 解读

An attempt is made to develop a best linear unbiased estimator of β in the model y = Xβ + u, with known singular covariance matrix V of u and restrictions on β. Two operational criteria for optimality are considered: minimum expected quadratic loss and minimum generalized variance. It is shown that these criteria lead to the same estimator, the well-known least-squares estimator, as developed by Theil (1971).