AbstractIn this paper we propose nonparametric estimates of the regression function and its derivative when it is only assumed a weak error's structure. We study their local and global asymptotic behaviour when we observe dependent trajectories
summary:Local polynomials are used to construct estimators for the value $m(x_{0})$ of the regressio...
In this thesis we develop inferential methods for time series models with weakly dependent errors ...
The aim of this paper is to show that existing estimators for the error distribution in nonparametri...
AbstractIn this paper we propose nonparametric estimates of the regression function and its derivati...
This paper studies the asymptotic properties of the nonlinear quantile regression model under genera...
This paper derives the asymptotic normality of the nonlinear quantile regression estimator with depe...
AbstractThe asymptotic distribution for the local linear estimator in nonparametric regression model...
AbstractWe investigate nonparametric curve estimation (including density, distribution, hazard, cond...
November 2009 (Revised: February 2010)We consider the nonparametric estimation of the regression fun...
summary:Linear relations, containing measurement errors in input and output data, are taken into acc...
The effect of errors in variables in nonparametric regression estimation is examined. To account for...
AbstractWe consider kernel density and regression estimation for a wide class of nonlinear time seri...
AbstractConsider the nonparametric regression model Yi(n) = g(xi(n)) + εi(n), i = 1, …, n, where g i...
The asymptotic properties of the least squares estimator are derived for a non regular nonlinear mod...
AbstractRegression quantiles provide a natural and powerful approach for robust analysis of the gene...
summary:Local polynomials are used to construct estimators for the value $m(x_{0})$ of the regressio...
In this thesis we develop inferential methods for time series models with weakly dependent errors ...
The aim of this paper is to show that existing estimators for the error distribution in nonparametri...
AbstractIn this paper we propose nonparametric estimates of the regression function and its derivati...
This paper studies the asymptotic properties of the nonlinear quantile regression model under genera...
This paper derives the asymptotic normality of the nonlinear quantile regression estimator with depe...
AbstractThe asymptotic distribution for the local linear estimator in nonparametric regression model...
AbstractWe investigate nonparametric curve estimation (including density, distribution, hazard, cond...
November 2009 (Revised: February 2010)We consider the nonparametric estimation of the regression fun...
summary:Linear relations, containing measurement errors in input and output data, are taken into acc...
The effect of errors in variables in nonparametric regression estimation is examined. To account for...
AbstractWe consider kernel density and regression estimation for a wide class of nonlinear time seri...
AbstractConsider the nonparametric regression model Yi(n) = g(xi(n)) + εi(n), i = 1, …, n, where g i...
The asymptotic properties of the least squares estimator are derived for a non regular nonlinear mod...
AbstractRegression quantiles provide a natural and powerful approach for robust analysis of the gene...
summary:Local polynomials are used to construct estimators for the value $m(x_{0})$ of the regressio...
In this thesis we develop inferential methods for time series models with weakly dependent errors ...
The aim of this paper is to show that existing estimators for the error distribution in nonparametri...
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