一般光滑参数化模型的无穷小稳健估计
Infinitesimally Robust Estimation in General Smoothly Parametrized Models
摘要：
本文描述了稳健统计的收缩邻域方法，它适用于一般的光滑参数化模型，特别是指数族。平等的一般性是通过面向对象的实现最优鲁棒估计。我们通过R包ROptEst和RobLox评估了文献中真实数据集的估计。
英文摘要：
We describe the shrinking neighborhood approach of Robust Statistics, which applies to general smoothly parametrized models, especially, exponential families. Equal generality is achieved by object oriented implementation of the optimally robust estimators. We evaluate the estimates on real datasets from literature by means of our R packages ROptEst and RobLox.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
