本文构造了非线性模型中参数的经验欧氏似然比统计量，并证明了该似然估计的强相合性和渐近正态性。
In this paper, empirical Euclidean likelihood ratio statistics are constructed for parametric in a nonlinear model. And prove strong consistency and asymptotic normality of the estimation.
本文证明了这种估计的强相合性，并讨论了其优效渐近正态性。
In this paper, we prove the strong consistency of the estimate, its efficiency asymptotic normality is discussed, too.
并且证明了在正态分布的假设下，该总体平均因果效应的极大似然估计是相合无偏且渐近正态的。
The maximum likelihood estimator for population average treatment effect is proved to be consistent, unbiased and asymptotically normal.
证明估计的强相合性和渐近正态性，给出似然比检验统计量的极限分布，并讨论基于精确分布的检验问题。
The limit distributions of estimators and likelihood ratio test are given, the strong consistency of estimators is also proved.
RSS样本下参数的极大似然估计(MLE)仍然是相合的和渐近正态的，而且RSS样本下参数的MLE较同样情况下SRS样本下参数的MLE更有效。
The maximum likelihood estimation (MLE) from an RSS sample is consistent an asymptotically normal and more efficient than its counterpart from an SRS sample.
第四章讨论了序贯指数模型的极大似然估计的强相合性和渐近正态性，并进行了证明。
In Chapter 4, we discuss and prove the consistency and asymptotic normality of maximum likelihood estimate to the exponential models.
第四章讨论了序贯指数模型的极大似然估计的强相合性和渐近正态性，并进行了证明。
In Chapter 4, we discuss and prove the consistency and asymptotic normality of maximum likelihood estimate to the exponential models.
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