讨论矩约束条件下的广义经验似然比统计量族以及相应的性质。
The family of generalized empirical likelihood ratio statistics with moment restrictions, which is a generalization of Baggerly, is investigated.
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.
最新的文献表明这个提出的修正似然比检验统计量在零假设下的渐近分布是比较简单，并且是容易应用的。
It is shown that the asymptotic null distribution of the modified likelihood ratio test proposed is derived and found to be relatively simple and easily applied.
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