Title: The asymptotic normality of the linear weighted estimator in nonparametric regression models
Abstract: Consider the following nonparametric regression model: Yni=g(xni)+εni,i=1,2,…,n,n≥1,where xni are known fixed design points from A⊂Rd for some positive integer d ⩾ 1, g( · ) is an unknown regression function defined on A and ϵni are random errors. Under some suitable conditions, the asymptotic normality of the linear weighted estimator of g in the nonparametric regression model based on ρ-mixing errors is established. The key techniques used in the paper are the Rosenthal type inequality and the Bernstein’s bigblock and small-block procedure. The result obtained in the paper generalizes the corresponding ones for some dependent sequences.
Publication Year: 2018
Publication Date: 2018-02-08
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 3
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot