On the correct regression function (in \(L_{2}\)) and its applications when the dimension of the covariate vector is random
type: Item
instance of: scholarly article
title: On the correct regression function (in \(L_{2}\)) and its applications when the dimension of the covariate vector is random
zbMATH Open document ID: 1253.62029
author: Majid Mojirsheibani
published in: Journal of Statistical Planning and Inference
publication date: 2012-09-04T00:00:00Z
Mathematics Subject Classification ID: 62G08
zbMATH DE Number: 6076813
zbMATH Keywords: kernel estimator
zbMATH Keywords: nonparametric
zbMATH Keywords: least squares
zbMATH Keywords: consistency
MaRDI profile type: Publication
full work available at URL: j.jspi.2012.03.017
OpenAlex ID: W1978392933
cites work: An equivalence theorem for \(L_ 1\) convergence of the kernel regression estimate
cites work: A distribution-free theory of nonparametric regression
cites work: Prediction from randomly right censored data
cites work: Statistical Classification with Missing Covariates
cites work: Q3330303
cites work: Asymptotic normality of the recursive kernel regression estimate under dependence conditions
cites work: Joint Asymptotic Distribution of the Estimated Regression Function at a Finite Number of Distinct Points
DOI: 10.1016/J.JSPI.2012.03.017
description: scientific article; zbMATH DE number 6076813