On the minimax robustness against correlation and heteroscedasticity of ordinary least squares among generalized least squares estimates of regression
普通最小二乘在广义最小二乘类中对相关性与异方差的极小极大稳健性
Summary We revisit a result according to which certain functions of covariance matrices are maximized at scalar multiples of the identity matrix. In a statistical context in which such functions measure loss, this says that the least favourable form of dependence is in fact independence, so that a procedure optimal for independent and identically distributed data can be minimax. In particular, the ordinary least squares estimate of a correctly specified regression response…