On Complete Consistency for the Estimator of Nonparametric Regression Model Based on Asymptotically Almost Negatively Associated Errors

2020 ◽  
Author(s):  
Aiting Shen ◽  
Siyao Zhang
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Nonparametric Regression Model ◽  
Negatively Associated ◽  
Complete Consistency ◽  
Model Based ◽  
Asymptotically Almost Negatively Associated

Complete consistency for the estimator of nonparametric regression model based on m-END errors

2021 ◽  
Vol 19 (1) ◽  
pp. 1197-1209
Author(s):  
Shui-Li Zhang ◽  
Tiantian Hou ◽  
Cong Qu
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Convergence Rates ◽  
Nonparametric Regression Model ◽  
Complete Consistency ◽  
Model Based ◽  
General Conditions

Abstract In this paper, we study the complete consistency for the estimator of nonparametric regression model based on m-END errors and obtain the convergence rates of the complete consistency under more general conditions. Finally, some simulations are illustrated to verify the validity of our results.

The Berry-Esseen bound of a wavelet estimator in non-randomly designed nonparametric regression model based on ANA errors

2020 ◽  
Vol 24 ◽  
pp. 21-38
Author(s):  
Xufei Tang ◽  
Xuejun Wang ◽  
Yi Wu ◽  
Fei Zhang
Keyword(s):  
Regression Model ◽  
Numerical Simulations ◽  
Nonparametric Regression ◽  
Random Variables ◽  
Nonparametric Regression Model ◽  
Wavelet Estimator ◽  
Negatively Associated ◽  
Model Based

Consider the nonparametric regression model Yni = g(tni) + εi, i = 1, 2, …, n,  n ≥ 1, where εi,  1 ≤ i ≤ n, are asymptotically negatively associated (ANA, for short) random variables. Under some appropriate conditions, the Berry-Esseen bound of the wavelet estimator of g(⋅) is established. In addition, some numerical simulations are provided in this paper. The results obtained in this paper generalize some corresponding ones in the literature.

scholarly journals Complete Consistency of the Estimator of Nonparametric Regression Models Based onρ~-Mixing Sequences

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Xuejun Wang ◽  
Fengxi Xia ◽  
Meimei Ge ◽  
Shuhe Hu ◽  
Wenzhi Yang
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Regression Models ◽  
Nearest Neighbor ◽  
Type Inequality ◽  
Nonparametric Regression Model ◽  
Complete Consistency ◽  
Model Based ◽  
Rosenthal Type Inequality ◽  
Mixing Sequences

We study the complete consistency for estimator of nonparametric regression model based onρ~-mixing sequences by using the classical Rosenthal-type inequality and the truncated method. As an application, the complete consistency for the nearest neighbor estimator is obtained.

scholarly journals Consistency properties for the wavelet estimator in nonparametric regression model with dependent errors

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qihui He ◽  
Mingming Chen
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
General Setting ◽  
Random Errors ◽  
Nonparametric Regression Model ◽  
Wavelet Estimator ◽  
Complete Consistency ◽  
Consistency Properties ◽  
General Conditions

AbstractIn this paper, we establish the pth mean consistency, complete consistency, and the rate of complete consistency for the wavelet estimator in a nonparametric regression model with m-extended negatively dependent random errors. We show that the best rates can be nearly $O(n^{-1/3})$ O ( n − 1 / 3 ) under some general conditions. The results obtained in the paper markedly improve and extend some corresponding ones to a much more general setting.

Sign in / Sign up

Export Citation Format

Share Document