On Berry-Esseen bound of wavelet estimators in nonparametric regression model under asymptotically negatively associated assumptions
From MaRDI portal
Publication:5082862
DOI10.1080/03610918.2019.1659966OpenAlexW2972155850MaRDI QIDQ5082862
Liwang Ding, Shengwei Yao, Ping Chen
Publication date: 21 June 2022
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2019.1659966
Berry-Esseen boundnonparametric regression modelwavelet estimatorsasymptotically negatively associated
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Wavelet estimation in varying coefficient models for censored dependent data
- A Berry-Esseen theorem and a law of the iterated logarithm for asymptotically negatively associated sequences
- Asymptotic normality of wavelet estimator in heteroscedastic regression model
- Moments of the maximum of normed partial sums of \(\rho^-\)-mixing random variables
- Fixed design regression for time series: Asymptotic normality
- Contributions to the theory of nonparametric regression, with application to system identification
- Uniformly asymptotic normality of the regression weighted estimator for negatively associated samples.
- Asymptotic normality of a wavelet estimator for asymptotically negatively associated errors
- Berry-Esseen bounds of weighted kernel estimator for a nonparametric regression model based on linear process errors under a LNQD sequence
- Formulae for mean integrated squared error of nonlinear wavelet-based density estimators
- Convergence rates in the strong laws of asymptotically negatively associated random fields
- On the consistency of the P-C estimator in a nonparametric regression model
- The Berry-Esseen bounds of the weighted estimator in a nonparametric regression model
- Inequalities of maximum of partial sums and weak convergence for a class of weak dependent random variables
- A Berry-Esseen Type Bound of Wavelet Estimator Under Linear Process Errors Based on a Strong Mixing Sequence
- Versions of Kernel-Type Regression Estimators
- Wavelet Methods for Curve Estimation
- Central limit theorems for asymptotically negatively associated random fields
- A functional central limit theorem for asymptotically negatively dependent random fields
This page was built for publication: On Berry-Esseen bound of wavelet estimators in nonparametric regression model under asymptotically negatively associated assumptions
