Open Access
2020 Exponential inequalities for dependent V-statistics via random Fourier features
Yandi Shen, Fang Han, Daniela Witten
Electron. J. Probab. 25: 1-18 (2020). DOI: 10.1214/20-EJP411


We establish exponential inequalities for a class of V-statistics under strong mixing conditions. Our theory is developed via a novel kernel expansion based on random Fourier features and the use of a probabilistic method. This type of expansion is new and useful for handling many notorious classes of kernels.


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Yandi Shen. Fang Han. Daniela Witten. "Exponential inequalities for dependent V-statistics via random Fourier features." Electron. J. Probab. 25 1 - 18, 2020.


Received: 21 March 2019; Accepted: 5 January 2020; Published: 2020
First available in Project Euclid: 29 January 2020

zbMATH: 07206400
MathSciNet: MR4059185
Digital Object Identifier: 10.1214/20-EJP411

Primary: 60F10

Keywords: dependent V-statistics , kernel expansion , random Fourier features , strong mixing condition

Vol.25 • 2020
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