摘要翻译：
通过一般估计方程(GEE)定义的参数可以通过最大经验似然(EL)来估计。Newey和Smith[Econometrica72(2004)219--255]最近证明了这种EL估计具有理想的高阶渐近性质，即它的$O(n^{-1}）$偏差很小，并且偏差校正后的EL是高阶有效的。虽然EL在模型正确指定时具有这些性质，但本文表明，当定义矩条件的函数无界时（甚至当它们的期望有界时），EL可能停止n根收敛。相比之下，相关的指数倾斜(ET)估计器避免了这个问题。本文表明，ET和EL估计可以自然地结合，得到一个称为指数倾斜经验似然(ETEL)的估计，它具有与EL相同的$O(n^{-1}）的偏差和相同的$O(n^{-2}）的方差，同时在模型不规范的情况下保持根n收敛。
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英文标题：
《Point estimation with exponentially tilted empirical likelihood》
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作者：
Susanne M. Schennach
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最新提交年份：
2007
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  Parameters defined via general estimating equations (GEE) can be estimated by maximizing the empirical likelihood (EL). Newey and Smith [Econometrica 72 (2004) 219--255] have recently shown that this EL estimator exhibits desirable higher-order asymptotic properties, namely, that its $O(n^{-1})$ bias is small and that bias-corrected EL is higher-order efficient. Although EL possesses these properties when the model is correctly specified, this paper shows that, in the presence of model misspecification, EL may cease to be root n convergent when the functions defining the moment conditions are unbounded (even when their expectations are bounded). In contrast, the related exponential tilting (ET) estimator avoids this problem. This paper shows that the ET and EL estimators can be naturally combined to yield an estimator called exponentially tilted empirical likelihood (ETEL) exhibiting the same $O(n^{-1})$ bias and the same $O(n^{-2})$ variance as EL, while maintaining root n convergence under model misspecification.
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PDF链接：
https://arxiv.org/pdf/0708.1874