The Berry-Esseen bounds of wavelet estimator for regression model whose errors form a linear process with a \(\rho\)-mixing

DOI10.1186/s13660-016-1036-xzbMath1335.60020OpenAlexW2332698561WikidataQ59467869 ScholiaQ59467869MaRDI QIDQ267695

Yongming Li, Liwang Ding

Publication date: 11 April 2016

Published in: Journal of Inequalities and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1186/s13660-016-1036-x

zbMATH Keywords

linear processesnormal approximationBerry-Esseen boundswavelet estimator\(\rho\)-mixing sequencesnonparametric regression model

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Central limit and other weak theorems (60F05) Generalized stochastic processes (60G20)

Related Items (4)

Berry–Esseen bound of wavelet estimators in heteroscedastic regression model with random errors ⋮ The asymptotic normality of the linear weighted estimator in nonparametric regression models ⋮ Strong convergence rates of multiple change-point estimator for ρ-mixing sequence ⋮ The Berry-Esseen bound of a wavelet estimator in non-randomly designed nonparametric regression model based on ANA errors

Cites Work

• Strong consistency of estimators in partially linear models for longitudinal data with mixing-dependent structure
• Berry-Esseen bounds for wavelet estimator in a regression model with linear process errors
• Uniformly asymptotic normality of the regression weighted estimator for negatively associated samples.
• Maximal inequalities for partial sums of \(\rho\)-mixing sequences
• On Strong Mixing Conditions for Stationary Gaussian Processes
• A BERRY-ESSEEN TYPE BOUND OF REGRESSION ESTIMATOR BASED ON LINEAR PROCESS ERRORS
• ASYMPTOTIC NORMALITY OF WAVELET ESTIMATOR OF REGRESSION FUNCTION UNDER NA ASSUMPTIONS
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