论文标题
部分可观测时空混沌系统的无模型预测
Concentration inequalities of MLE and robust MLE
论文作者
论文摘要
最大似然估计器（MLE）在统计和机器学习中起着重要作用。在本文中，对于I.I.D.变量，我们仅在指数力矩条件下才能获得MLE的恒定浓度不平等和甲骨文不平等。此外，在健壮的环境中，在第二矩条件下得出了对数截断的最大似然估计量的次高斯型甲骨文不等式。
The Maximum Likelihood Estimator (MLE) serves an important role in statistics and machine learning. In this article, for i.i.d. variables, we obtain constant-specified and sharp concentration inequalities and oracle inequalities for the MLE only under exponential moment conditions. Furthermore, in a robust setting, the sub-Gaussian type oracle inequalities of the log-truncated maximum likelihood estimator are derived under the second-moment condition.
