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
A noniterative method of estimation is presented in a simple linear regression model where the indep...
International audienceIn this article, we propose some new generalizations of M-estimation procedure...
General methods for testing the fit of a parametric function are proposed. The idea underlying each ...
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...
Assume that we have two populations (X 1,Y 1) and (X 2,Y 2) satisfying two general nonparametric reg...
This book addresses the testing of hypothses in non-parametric models in the specific case of censor...
In this article, we introduce a procedure to test the equality of regression functions when the resp...
We address the issue of lack-of-fit testing for a parametric quantile regression. We propose a simpl...
A new lack-of-fit test for quantile regression models will be presented for the case where the respo...
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
The aim of this book is to estimate the conditional mean of some functions depending on the respon...
This paper introduces the operating of the selection criteria for right-censored nonparametric regre...
The nonparametric censored regression model, with a fixed, known censoring point (normalized to zero...
We consider a k-nearest neighbor-based nonparametric lack-of-fit test of constant regression in pres...
A noniterative method of estimation is presented in a simple linear regression model where the indep...
International audienceIn this article, we propose some new generalizations of M-estimation procedure...
General methods for testing the fit of a parametric function are proposed. The idea underlying each ...
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...
Assume that we have two populations (X 1,Y 1) and (X 2,Y 2) satisfying two general nonparametric reg...
This book addresses the testing of hypothses in non-parametric models in the specific case of censor...
In this article, we introduce a procedure to test the equality of regression functions when the resp...
We address the issue of lack-of-fit testing for a parametric quantile regression. We propose a simpl...
A new lack-of-fit test for quantile regression models will be presented for the case where the respo...
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
The aim of this book is to estimate the conditional mean of some functions depending on the respon...
This paper introduces the operating of the selection criteria for right-censored nonparametric regre...
The nonparametric censored regression model, with a fixed, known censoring point (normalized to zero...
We consider a k-nearest neighbor-based nonparametric lack-of-fit test of constant regression in pres...
A noniterative method of estimation is presented in a simple linear regression model where the indep...
International audienceIn this article, we propose some new generalizations of M-estimation procedure...
General methods for testing the fit of a parametric function are proposed. The idea underlying each ...