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良好与最优的岭估计量AI 解读

In generalized ridge estimation, the components of the ordinary least squares (OLS) regression coefficient vector which lie along the principal axes of the given regressor data are rescaled using known ridge factors. Generalizing a result of Swindel and Chapman, it is shown that, if each ridge factor is nonstochastic, nonnegative, and less than one, then there is at most one unknown direction in regression coefficient space along which ridge coefficients have larger mean…

残差的最优性：普通、加权与有偏最小二乘的比较AI 解读

In the general linear model with observations not necessarily uncorrelated or homoscedastic, Gauss-Markov regression coefficients are superior to ordinary unweighted least squares in the well known BLU sense if the model is correct. However, it is shown that there is a weaker, but always applicable, minimum overall mean squared error sense in which Gauss-Markov residuals and biased residuals are inferior to ordinary least squares residuals as estimators of possible…

残差最优性：普通、加权与有偏最小二乘的比较AI 解读

In the general linear model with observations not necessarily uncorrelated or homoscedastic, Gauss-Markov regression coefficients are superior to ordinary unweighted least squares in the well known BLU sense if the model is correct. However, it is shown that there is a weaker, but always applicable, minimum overall mean squared error sense in which Gauss-Markov residuals and biased residuals are inferior to ordinary least squares residuals as estimators of possible…