Exponential inequalities for dependent V-statistics via random Fourier features | Zendy

Yandi Shen | Zendy; Fang Han | Zendy; Daniela Witten | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Exponential inequalities for dependent V-statistics via random Fourier features

Author(s) -

Yandi Shen,

Fang Han,

Daniela Witten

Publication year - 2020

Publication title -

electronic journal of probability

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.666

H-Index - 53

ISSN - 1083-6489

DOI - 10.1214/20-ejp411

Subject(s) - mathematics , fourier series , exponential function , mixing (physics) , fourier transform , inequality , class (philosophy) , kernel (algebra) , probabilistic logic , statistics , pure mathematics , mathematical analysis , artificial intelligence , computer science , physics , quantum mechanics

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research