Consider a heteroscedastic regression model Y=m(X) +σ(X)ε, where the functions m and σ are “smooth”, and ε is independent of X. An estimator of the distribution of ε based on non-parametric regression residuals is proposed and its weak convergence is obtained. Applications to prediction intervals and goodness-of-fit tests are discussed
Our article presents a general treatment of the linear regression model, in which the error distribu...
Consider the regression problem with a response variable Y and with a d-dimensional feature vector X...
This paper deals with the nonparametric estimation in heterosce-dastic regression Yi = f(Xi) + ξi, i...
In this paper we consider the estimation of the error distribution in a heteroscedastic nonparametri...
AbstractIn this paper we consider the estimation of the error distribution in a heteroscedastic nonp...
Consider a heteroscedastic regression model Y = m(X) + σ(X)ε, where m(X) = E(Y|X) and σ2 (X) = Var(Y...
When dealing with heteroskedastic models Y = μ(X) + c in econometrics and other disciplines, situati...
The aim of non-parametric regression is to model the behaviour of a response vector Y in terms of an...
This thesis considers the problem of estimating limited dependent variable models when the latent re...
We propose a residual-based empirical distribution function to estimate the distribution function o...
This paper addresses the problem of fitting a known distribution to the innovation distribution in a...
We consider nonparametric identi\u85cation and estimation of truncated regression models with unknow...
Let (X, Y) be a random vector, where Y denotes the variable of interest, possibly subject to random ...
AbstractThis paper is concerned with the linear regression model in which the variance of the depend...
Consider the nonparametric regression model Y=m(X) + ε, where the function m is smooth but unknown, ...
Our article presents a general treatment of the linear regression model, in which the error distribu...
Consider the regression problem with a response variable Y and with a d-dimensional feature vector X...
This paper deals with the nonparametric estimation in heterosce-dastic regression Yi = f(Xi) + ξi, i...
In this paper we consider the estimation of the error distribution in a heteroscedastic nonparametri...
AbstractIn this paper we consider the estimation of the error distribution in a heteroscedastic nonp...
Consider a heteroscedastic regression model Y = m(X) + σ(X)ε, where m(X) = E(Y|X) and σ2 (X) = Var(Y...
When dealing with heteroskedastic models Y = μ(X) + c in econometrics and other disciplines, situati...
The aim of non-parametric regression is to model the behaviour of a response vector Y in terms of an...
This thesis considers the problem of estimating limited dependent variable models when the latent re...
We propose a residual-based empirical distribution function to estimate the distribution function o...
This paper addresses the problem of fitting a known distribution to the innovation distribution in a...
We consider nonparametric identi\u85cation and estimation of truncated regression models with unknow...
Let (X, Y) be a random vector, where Y denotes the variable of interest, possibly subject to random ...
AbstractThis paper is concerned with the linear regression model in which the variance of the depend...
Consider the nonparametric regression model Y=m(X) + ε, where the function m is smooth but unknown, ...
Our article presents a general treatment of the linear regression model, in which the error distribu...
Consider the regression problem with a response variable Y and with a d-dimensional feature vector X...
This paper deals with the nonparametric estimation in heterosce-dastic regression Yi = f(Xi) + ξi, i...
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