The Asymptotic Efficiency of Conditional Likelihood Methods
本文研究含多余参数模型中条件似然方法的效率，提出渐近弱辅助性概念，证明在条件统计量满足该性质时，条件极大似然估计和条件得分检验与无条件方法渐近等价且有效，并验证了指数族分布下的适用性。
This paper concerns the efficiency of the conditional likelihood method for inference in models which include nuisance parameters. A new concept of ancillarity, asymptotic weak ancillarity, is introduced. It is shown that the conditional maximum likelihood estimator and the conditional score test of θ, the parameter of interest, are asymptotically equivalent to their unconditional counterparts, and hence are asymptotically efficient, provided that the conditioning statistic is asymptotically weakly ancillary. The key assumption that the conditioning statistic is asymptotically weakly ancillary is verified when the underlying distribution is from exponential families. Some illustrative examples are given.