A large deviation inequality for \(\beta\)-mixing time series and its applications to the functional kernel regression model

DOI10.1016/j.spl.2017.09.013zbMath1380.62235arXiv1701.05380OpenAlexW2728987053MaRDI QIDQ680473

Johannes T. N. Krebs

Publication date: 23 January 2018

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1701.05380

zbMATH Keywords

\(\beta\)-mixingtime seriesfunctional data analysisnonparametric statisticslarge deviation inequalityasymptotic inequalities

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09) Large deviations (60F10)

Related Items (2)

Non-parametric regression for spatially dependent data with wavelets ⋮ Concentration inequalities for kernel density estimators under uniform mixing

Cites Work

• Unnamed Item
• Basic properties of strong mixing conditions. A survey and some open questions
• Mixing: Properties and examples
• A Bernstein-type inequality for \(U\)-statistics and \(U\)-processes
• Coupling for \(\tau\)-dependent sequences and applications
• A distribution-free theory of nonparametric regression
• Large deviations and strong mixing
• Probability inequalities for bounded random vectors
• Bernstein inequality and moderate deviations under strong mixing conditions
• [https://portal.mardi4nfdi.de/wiki/Publication:3038407 Propri�t�s de convergence presque compl�te du pr�dicteur � noyau]
• On the Validity of the Bootstrap in Non-Parametric Functional Regression
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• On Strong Mixing Conditions for Stationary Gaussian Processes
• Advances on asymptotic normality in non-parametric functional time series analysis
• Probability Inequalities for Sums of Bounded Random Variables
• NONPARAMETRIC REGRESSION ON FUNCTIONAL DATA: INFERENCE AND PRACTICAL ASPECTS
• Convergence of Distributions Generated by Stationary Stochastic Processes
• Some Limit Theorems for Stationary Processes

This page was built for publication: A large deviation inequality for \(\beta\)-mixing time series and its applications to the functional kernel regression model