论文标题
一对新的先验，用于改善和扩展条件MLE
A Pair of Novel Priors for Improving and Extending the Conditional MLE
论文作者
论文摘要
通过引入一对先验，提出了一个旨在改善条件MLE的贝叶斯估计量。在先验下通过后模式解释条件MLE之后，我们在相应的先验下通过后均值定义了有希望的估计器。先验等同于熟悉模型中的参考先验。本方法的优点包括诱导估计器的两个不同的最优性能，各种扩展的易感性以及有限样本量的可能治疗方法。讨论和批评现有的方法。
A Bayesian estimator aiming at improving the conditional MLE is proposed by introducing a pair of priors. After explaining the conditional MLE by the posterior mode under a prior, we define a promising estimator by the posterior mean under a corresponding prior. The prior is equivalent to the reference prior in familiar models. Advantages of the present approach include two different optimality properties of the induced estimator, the ease of various extensions and the possible treatments for a finite sample size. The existing approaches are discussed and critiqued.
