摘要翻译：
许多推理情况的中心是参数有理函数的估计。统计和计量经济学中的主流是基于插件方法来估计这些数量，而没有考虑推理情况的主要目标。我们提出了贝叶斯最小期望损失(MELO)方法，明确关注兴趣函数，并计算其频率变异性。MELO估计量的渐近性质类似于插件方法。然而，仿真实验表明，我们的方案在小样本和有噪声模型的情况下是更好的。此外，我们在应用程序中观察到，当数据集信息不是很丰富时，我们的方法比经常使用的替代方法提供更低的标准错误。
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英文标题：
《Focused econometric estimation for noisy and small datasets: A Bayesian
  Minimum Expected Loss estimator approach》
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作者：
Andres Ramirez-Hassan and Manuel Correa-Giraldo
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  Central to many inferential situations is the estimation of rational functions of parameters. The mainstream in statistics and econometrics estimates these quantities based on the plug-in approach without consideration of the main objective of the inferential situation. We propose the Bayesian Minimum Expected Loss (MELO) approach focusing explicitly on the function of interest, and calculating its frequentist variability. Asymptotic properties of the MELO estimator are similar to the plug-in approach. Nevertheless, simulation exercises show that our proposal is better in situations characterized by small sample sizes and noisy models. In addition, we observe in the applications that our approach gives lower standard errors than frequently used alternatives when datasets are not very informative.
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PDF链接：
https://arxiv.org/pdf/1809.06996