International audienceThe objective is to construct a tool to test the validity of a regression model. For this, we have studied some omnibus tests of goodness-of-fit. Our study focuses on the regression function in the regression model. These tests can be ``directional'' in that they are designed to detect departures from mainly one given assumption of the model (regression function, variance function or error) or global with the conditional distribution function. We have compared, through simulations, different nonparametric methods to test the validity of the model. Two methods are directional and one is global. The establishment of such statistical tests require nonparametric estimators and the use of wild bootstrap methods for the simu...
Consider the nonparametric regression model Y = m(X) + ε, where the function m is smooth, but unknow...
We propose a framework for constructing goodness-of-fit tests in both low and high dimensional linea...
Consider a heteroscedastic regression model Y = m(X) + s(X) e, where m(X) = E(Y vertical bar X) and ...
International audienceThe objective is to construct a tool to test the validity of a regression mode...
This paper presents a goodness-of-fit test for parametric regression models with scalar response and...
summary:Test procedures are constructed for testing the goodness-of-fit in parametric regression mod...
This paper utilizes the bootstrap to construct tests using the measures for goodness-of-fit for nonn...
The authors propose a goodness-of-fit test for parametric regression models when the response variab...
Doctor of PhilosophyDepartment of StatisticsWeixing SongIn this dissertation, goodness-of-fit tests ...
Consider the following semiparametric transformation model Λθ (Y) = m(X) + ε, where X is a d-dimensi...
grantor: University of TorontoThe statistical analysis of dichotomous outcome variables is...
International audienceWe introduce a goodness-of-fit test for statistical models about the condition...
Consider the nonparametric location-scale regression model Y = m(X) + sigma(X)epsilon, where the err...
This paper proposes several tests of restricted specification in nonparametric instrumental regressi...
General methods for testing the fit of a parametric function are proposed. The idea underlying each ...
Consider the nonparametric regression model Y = m(X) + ε, where the function m is smooth, but unknow...
We propose a framework for constructing goodness-of-fit tests in both low and high dimensional linea...
Consider a heteroscedastic regression model Y = m(X) + s(X) e, where m(X) = E(Y vertical bar X) and ...
International audienceThe objective is to construct a tool to test the validity of a regression mode...
This paper presents a goodness-of-fit test for parametric regression models with scalar response and...
summary:Test procedures are constructed for testing the goodness-of-fit in parametric regression mod...
This paper utilizes the bootstrap to construct tests using the measures for goodness-of-fit for nonn...
The authors propose a goodness-of-fit test for parametric regression models when the response variab...
Doctor of PhilosophyDepartment of StatisticsWeixing SongIn this dissertation, goodness-of-fit tests ...
Consider the following semiparametric transformation model Λθ (Y) = m(X) + ε, where X is a d-dimensi...
grantor: University of TorontoThe statistical analysis of dichotomous outcome variables is...
International audienceWe introduce a goodness-of-fit test for statistical models about the condition...
Consider the nonparametric location-scale regression model Y = m(X) + sigma(X)epsilon, where the err...
This paper proposes several tests of restricted specification in nonparametric instrumental regressi...
General methods for testing the fit of a parametric function are proposed. The idea underlying each ...
Consider the nonparametric regression model Y = m(X) + ε, where the function m is smooth, but unknow...
We propose a framework for constructing goodness-of-fit tests in both low and high dimensional linea...
Consider a heteroscedastic regression model Y = m(X) + s(X) e, where m(X) = E(Y vertical bar X) and ...