A class of estimators of the parameters in linear regression with censored data
type: Item
instance of: scholarly article
title: A class of estimators of the parameters in linear regression with censored data
zbMATH Open document ID: 0669.62048
DOI: 10.1007/BF02007667
published in: Acta Mathematicae Applicatae Sinica. English Series
publication date: 1987-01-01T00:00:00Z
review text: This paper considers the linear regression model \(y_ i=\alpha +\beta x_ i+\epsilon_ i\) \((n=1,...,n)\) where the \(y_ i's\) may be censored and therefore not completely observable. A new class of estimators is proposed (it includes several estimators previously considered in the literature). The paper gives sufficient conditions under which the proposed estimators are strongly consistent and asymptotically normal.
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62F12
zbMATH DE Number: 4096604
zbMATH Keywords: censored data
zbMATH Keywords: strongly consistent
zbMATH Keywords: asymptotically normal
author: Zukang Zheng
MaRDI profile type: Publication
cites work: Least squares regression with censored data
cites work: Nonparametric Estimation from Incomplete Observations
cites work: Linear regression with censored data
cites work: Consistency results for linear regression with censored data
cites work: Regression analysis with randomly right-censored data
cites work: Asymptotic Normality and Consistency of the Least Squares Estimators for Families of Linear Regressions
cites work: Strong consistency of least squares estimates in multiple regression II
cites work: A law of the iterated logarithm for double arrays of independent random variables with applications to regression and time series models
full work available at URL: https://doi.org/10.1007/bf02007667
OpenAlex ID: W2000587489
description: scientific article; zbMATH DE number 4096604