Shrinkage Estimators of Relative Potency
研究了基于贝叶斯论证的对数相对效力收缩估计量的有限样本和无限样本性质，证明其在所有有限样本下均方风险优于最大似然估计，且渐近行为相同。
Abstract This article examines the finite and infinite sample properties of the shrinkage estimator, motivated by a Bayesian argument, for the log relative potency, proposed in an earlier paper by Kim, Carter, and Hubert. This estimator can be written in closed form and is shown to have finite mean and finite variance in finite samples. As a consequence, this shrinkage estimator has finite frequentist risk, which is an improvement over the usual maximum likelihood estimator, for all finite sample sizes. Furthermore, it is shown that this estimator asymptotically behaves the same as the usual maximum likelihood estimator.