James-Stein (JS) estimators have been described as showing the inadequacy of maximum likelihood estimation when assessed using mean square error (MSE). We claim the problem is not with maximum likelihood (ML) but with MSE. When MSE is replaced with a measure $\Lambda$ of the information utilized by a statistic, likelihood based methods are superior. The information measure $\Lambda$ describes not just point estimators but extends to Fisher's view of estimation so that we not only reconsider how estimators are assessed but also how we define an estimator. Fisher information and his views on the role of parameters, interpretation of probability, and logic of statistical inference fit well with $\Lambda$ as measure of information.

翻译：James-Stein (JS) 估计量常被用来证明以均方误差 (MSE) 评估时最大似然估计的不足。我们认为问题不在于最大似然 (ML) 估计本身，而在于 MSE 这一评估标准。当用衡量统计量所利用信息量的度量 $\Lambda$ 替代 MSE 时，基于似然的方法表现出更优性。信息度量 $\Lambda$ 不仅适用于点估计量的评估，还可扩展至 Fisher 的估计理论框架，从而使我们不仅重新思考如何评估估计量，也重新审视估计量的定义方式。Fisher 信息及其关于参数作用、概率解释与统计推断逻辑的观点，与作为信息量度的 $\Lambda$ 高度契合。