Asymptotic analysis of higher-order scattering transform of Gaussian processes

DOI10.1214/22-EJP766zbMath1492.60142arXiv2108.08794OpenAlexW3195649913MaRDI QIDQ2136093

Yuan-Chung Sheu, Hau-Tieng Wu, Gi-Ren Liu

Publication date: 10 May 2022

Published in: Electronic Journal of Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2108.08794

zbMATH Keywords

Malliavin calculusStein's methodwavelet transformscattering transformWiener-Itô decompositionscaling limits

Mathematics Subject Classification ID

Random fields (60G60) Gaussian processes (60G15) Inference from stochastic processes and spectral analysis (62M15) Stochastic calculus of variations and the Malliavin calculus (60H07)

Related Items (1)

Central and Noncentral Limit Theorems Arising from the Scattering Transform and Its Neural Activation Generalization

Uses Software

• ScatNet

Cites Work

• Unnamed Item
• Multiple Wiener-Itô integrals. With applications to limit theorems
• Regularization and integral representations of Hermite processes
• Stein's method on Wiener chaos
• Invariance principles for homogeneous sums: universality of Gaussian Wiener chaos
• Intermittent process analysis with scattering moments
• Analysis of time-frequency scattering transforms
• Normal Approximations with Malliavin Calculus
• Group Invariant Scattering
• A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
• Deep Scattering Spectrum
• Deep Haar scattering networks
• Lipschitz properties for deep convolutional networks
• Long-Range Dependence and Self-Similarity
• Stationary random distributions

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