Add to ORKGConsider the heteroscedastic polynomial regression model $ Y = \beta_0 + \beta_1X + ... + \beta_pX^p + \sqrt{Var(Y|X)}\epsilon $, where \epsilon is independent of X, and Y is subject to random censoring. Provided that the censoring on Y is 'light' in some region of X, we construct a least squares estimator for the regression parameters whose asymptotic bias is shown to be as small as desired. The least squares estimator is defined as a functional of the Van Keilegom and Akritas (1999) estimator of the bivariate distribution $P(X \leq x, Y \leq y)$, and its asymptotic normality is obtained
Let (X, Y) be a pair of random variables such that X = (X1,..., Xd) ranges over a nondegenerate comp...
AbstractIn a linear model Y = Xβ + Z a linear functional β → γ′β is to be estimated under squared er...
This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of t...
Consider the heteroscedastic polynomial regression model $ Y = \beta_0 + \beta_1X + ... + \beta_pX^p...
[[abstract]]The ordinary least squares (OLS) method is popular for analyzing linear regression model...
Consider the polynomial regression model Y = (beta)0 + beta(1) X + center dot center dot center dot ...
AbstractThis paper is concerned with the linear regression model in which the variance of the depend...
AbstractKoul, Susarla and Van Ryzin (1981, Ann. Statist. 9, 1276-1288) proposed a generalization of ...
Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regressi...
Suppose the random vector (X; Y ) satises the regression model Y = m(X) + σ(X)ε, where m(.) = E(Y|.)...
In a linear model $Y=X\beta +Z$ a linear functional $\beta \mapsto \gamma '\beta$ is to be estimated...
International audienceThe problem of estimating a nonlinear regression model, when the dependent var...
We give the limiting distribution of the least squares estimator in the polynomial regression model ...
This paper proposes an alternative to maximum likelihood estimation of the parameters of the censore...
This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of t...
Let (X, Y) be a pair of random variables such that X = (X1,..., Xd) ranges over a nondegenerate comp...
AbstractIn a linear model Y = Xβ + Z a linear functional β → γ′β is to be estimated under squared er...
This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of t...
Consider the heteroscedastic polynomial regression model $ Y = \beta_0 + \beta_1X + ... + \beta_pX^p...
[[abstract]]The ordinary least squares (OLS) method is popular for analyzing linear regression model...
Consider the polynomial regression model Y = (beta)0 + beta(1) X + center dot center dot center dot ...
AbstractThis paper is concerned with the linear regression model in which the variance of the depend...
AbstractKoul, Susarla and Van Ryzin (1981, Ann. Statist. 9, 1276-1288) proposed a generalization of ...
Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regressi...
Suppose the random vector (X; Y ) satises the regression model Y = m(X) + σ(X)ε, where m(.) = E(Y|.)...
In a linear model $Y=X\beta +Z$ a linear functional $\beta \mapsto \gamma '\beta$ is to be estimated...
International audienceThe problem of estimating a nonlinear regression model, when the dependent var...
We give the limiting distribution of the least squares estimator in the polynomial regression model ...
This paper proposes an alternative to maximum likelihood estimation of the parameters of the censore...
This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of t...
Let (X, Y) be a pair of random variables such that X = (X1,..., Xd) ranges over a nondegenerate comp...
AbstractIn a linear model Y = Xβ + Z a linear functional β → γ′β is to be estimated under squared er...
This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of t...