A compromise criterion for weighted least squares estimates

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When independent errors in a linear model have non-identity covariance, the ordinary least squares estimate of the model coefficients is less efficient than the weighted least squares estimate. However, the practical application of weighted least squares is challenging due to its reliance on the unknown error covariance matrix. Although feasible weighted least squares estimates, which use an approximation of this matrix, often outperform the ordinary least squares estimate in terms of efficiency, this is not always the case. In some situations, feasible weighted least squares can be less efficient than ordinary least squares. The comparison between these two estimates has significant implications for the application of regression analysis in varied fields, yet such a comparison remains an unresolved challenge despite its seemingly straightforward nature. In this study, we directly address this challenge by identifying the conditions under which feasible weighted least squares estimates using fixed weights demonstrate greater efficiency than the ordinary least squares estimate. These conditions provide guidance for the design of feasible estimates using random weights. They also shed light on how certain robust regression estimates behave with respect to the linear model with normal errors of unequal variance.

heteroscedasticity

M-estimation

linear regression

quasi-convexity 

International Statistical Institute