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.
翻译:通过引入一对前科,提出旨在改进有条件最低教育等级的巴耶斯估计员。在用后代模式在前一种前科解释有条件最低教育等级之后,我们用后代模式在前一种前科下定义了有希望的后代标准。前代标准相当于熟悉模型之前的参考标准。当前方法的优点包括诱导估计员两种不同的最佳性能、各种扩展的易易性以及有限的抽样大小的可能处理方法。现有方法得到了讨论和批评。