Add to ORKGInternational audienceWe develop two kernel smoothing based tests of a parametric mean-regression model against a nonparametric alternative when the response variable is right-censored. The new test statistics are inspired by the synthetic data and the weighted least squares approaches for estimating the parameters of a (non)linear regression model under censoring. The asymptotic critical values of our tests are given by the quantiles of the standard normal law. The tests are consistent against fixed alternatives, local Pitman alternatives and uniformly over alternatives in H\"{o}lder classes of functions of known regularity
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
summary:This paper proposes a bias reduction of the coefficients' estimator for linear regression mo...
We consider the random design nonparametric regression problem when the response variable is subject...
International audienceWe develop two kernel smoothing based tests of a parametric mean-regression mo...
The authors propose a goodness-of-fit test for parametric regression models when the response variab...
A new lack-of-fit test for quantile regression models will be presented for the case where the respo...
International audienceIn this article, we propose some new generalizations of M-estimation procedure...
Assume that we have two populations (X 1,Y 1) and (X 2,Y 2) satisfying two general nonparametric reg...
We consider regression models with randomly right-censored responses. We propose new estimators of t...
Parametrically guided nonparametric regression is an appealing method that can reduce the bias of a ...
This book addresses the testing of hypothses in non-parametric models in the specific case of censor...
We address the issue of lack-of-fit testing for a parametric quantile regression. We propose a simpl...
In this paper we study the goodness-of-fit test introduced by Fortiana and Grané (2003) and Grané (2...
In this article, we introduce a procedure to test the equality of regression functions when the resp...
The main goal of this work it is to present a review of the existing methods to deal with the goodne...
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
summary:This paper proposes a bias reduction of the coefficients' estimator for linear regression mo...
We consider the random design nonparametric regression problem when the response variable is subject...
International audienceWe develop two kernel smoothing based tests of a parametric mean-regression mo...
The authors propose a goodness-of-fit test for parametric regression models when the response variab...
A new lack-of-fit test for quantile regression models will be presented for the case where the respo...
International audienceIn this article, we propose some new generalizations of M-estimation procedure...
Assume that we have two populations (X 1,Y 1) and (X 2,Y 2) satisfying two general nonparametric reg...
We consider regression models with randomly right-censored responses. We propose new estimators of t...
Parametrically guided nonparametric regression is an appealing method that can reduce the bias of a ...
This book addresses the testing of hypothses in non-parametric models in the specific case of censor...
We address the issue of lack-of-fit testing for a parametric quantile regression. We propose a simpl...
In this paper we study the goodness-of-fit test introduced by Fortiana and Grané (2003) and Grané (2...
In this article, we introduce a procedure to test the equality of regression functions when the resp...
The main goal of this work it is to present a review of the existing methods to deal with the goodne...
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
summary:This paper proposes a bias reduction of the coefficients' estimator for linear regression mo...
We consider the random design nonparametric regression problem when the response variable is subject...