Wiener Chaos Approach to Optimal Prediction

DOI10.1080/01630563.2015.1065273zbMath1334.60057arXiv1411.3032OpenAlexW2964191187MaRDI QIDQ2795088

Alon Kipnis, Daniel Alpay

Publication date: 18 March 2016

Published in: Numerical Functional Analysis and Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1411.3032

zbMATH Keywords

fractional Brownian motionGaussian processesHermite polynomialsoptimal predictionWiener chaosstationary increments

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Gaussian processes (60G15) Fractional processes, including fractional Brownian motion (60G22) Stationary stochastic processes (60G10) White noise theory (60H40) Prediction theory (aspects of stochastic processes) (60G25) Hardy spaces (30H10)

Related Items (1)

Dynamical properties of endomorphisms, multiresolutions, similarity and orthogonality relations

Cites Work

• Hilbert spaces of analytic functions, inverse scattering and operator models. I
• Dilations and Mehler's kernel
• On an extension problem, generalized Fourier analysis, and an entropy formula
• Stochastic analysis of the fractional Brownian motion
• Krein's spectral theory and the Paley-Wiener expansion for fractional Brownian motion
• Simulation of BSDEs by Wiener chaos expansion
• Wiener chaos expansions and numerical solutions of randomly forced equations of fluid mechanics
• Representations of fractional Brownian motion using vibrating strings
• Über die Struktur stationärer zufälliger Funktionen
• An introduction to white–noise theory and Malliavin calculus for fractional Brownian motion
• On the prediction of fractional Brownian motion
• Unnamed Item
• Unnamed Item

This page was built for publication: Wiener Chaos Approach to Optimal Prediction