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arXiv 2308.02007math.PR
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随机多项式间全变差距离的改进界

Improved bounds for the total variation distance between stochastic polynomials

Egor Kosov, Anastasia Zhukova

AI总结:

本文研究满足Doeblin型条件的随机向量中特殊多项式间全变差距离的上界,基于Nikolskii--Besov型平滑性结果,改进了Nourdin--Poly和Bally--Caramellino的先前估计。

摘要:

本文研究了满足Doeblin型条件的随机向量中特殊形式多项式间全变差距离的上界。我们的方法基于关于对数凹随机向量中多项式分布密度的Nikolskii--Besov型平滑性的最新结果。本文的主要结果改进了先前Nourdin--Poly和Bally--Caramellino所得的估计。

英文摘要:

The paper studies upper bounds for the total variation distance between two polynomials of a special form in random vectors satisfying the Doeblin-type condition. Our approach is based on the recent results concerning Nikolskii--Besov-type smoothness of distribution densities of polynomials in logarithmically concave random vectors. The main results of the paper improve previously obtained estimates of Nourdin--Poly and Bally--Caramellino.

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。

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