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非正态误差与二次损失下的位置参数估计AI 解读

Assuming a statistical model in which the joint distribution of the unobservable errors is drawn from independent univariate Student t's that are identically and symmetrically distributed, the sampling performance of traditional robust estimators and a family of Stein-like estimators are compared and evaluated. These results suggest that under thick-tailed distributions, the relative sampling performances and risk characteristics for a range of nonconventional Stein…

球对称密度位置向量的极小极大估计AI 解读

The estimation of K ( K ≥ 3) location parameters is considered under quadratic loss when the coordinates of the best invariant estimators are spherically symmetrically distributed. Under these stochastic mechanisms traditional Stein estimators are evaluated for finite samples and shown to have a risk performance superior to some conventional rules.