为什么Minimax不是那么悲观
Why minimax is not that pessimistic
摘要：
在非参数统计中，极小极大收敛速率提供了估计过程的最优性准则。然而，这种经典的观点是有争议的，因为它需要寻找最坏的行为达到了一个估计程序在一个给定的空间。本文的目的是表明，这是不合理的极小极大风险往往符合一个通用的。在这里，我们感兴趣的收敛速率达到一些经典的估计几乎每一个，在流行的意义上，功能在Besov空间。
英文摘要：
In nonparametric statistics an optimality criterion for estimation procedures is provided by the minimax rate of convergence. However this classical point of view is subject to controversy as it requires to look for the worst behaviour reached by an estimation procedure in a given space. The purpose of this paper is to show that this is not justified as the minimax risk often coincides with a generic one. We are here interested in the rate of convergence attained by some classical estimators on almost every, in the sense of prevalence, function in a Besov space.
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。
