Abstract In this note we consider several goodness-of-fit tests for model specification in nonparametric regression models which are based on kernel methods. In order to circumvent the problem of choosing a bandwidth for the corresponding test statistic we propose to consider the statistics as stochastic processes indexed with bandwidths proportional to the asymptotically optimal bandwidth for the estimation of the regression function. We prove weak convergence of these processes to centered Gaussian processes and suggest to use functionals of these processes as test statistics for the problem of model specification. A bootstrap test is proposed to obtain a good approximation of the nominal level. The results are illustrated by means of a s...
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
We examine a consistent test for the correct specification of a regression function with dependent d...
The unknown error density of a nonparametric regression model is approximated by a mixture of Gaussi...
In this note we consider several goodness-of-fit tests for model specification in non-parametric reg...
In this note, we consider several goodness-of-fit tests for model specifica-tion in nonparametric re...
In this note we consider several goodness-of-fit tests for model specification in nonparametric regr...
A general model specification test of a parametric model against a nonparametric or semiparametric a...
A general model specication test of a parametric model against a nonparametric or semiparametric alt...
This paper considers a nonparametric time series regression model with a nonstationary regressor. We...
We propose a test for model specification of a parametric diffusion process based on a kernel estima...
We propose a test for model specification of a parametric diffusion process based on a kernel estima...
Although estimation and testing are different statistical problems, if we want to use a test statist...
© Institute of Mathematical Statistics, 2009This paper considers a class of nonparametric autoregres...
AbstractThis paper investigates performance of nonparametric kernel regression and its associated ba...
Semiparametric and nonparametric estimators are becoming indispensable tools in applied econometric...
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
We examine a consistent test for the correct specification of a regression function with dependent d...
The unknown error density of a nonparametric regression model is approximated by a mixture of Gaussi...
In this note we consider several goodness-of-fit tests for model specification in non-parametric reg...
In this note, we consider several goodness-of-fit tests for model specifica-tion in nonparametric re...
In this note we consider several goodness-of-fit tests for model specification in nonparametric regr...
A general model specification test of a parametric model against a nonparametric or semiparametric a...
A general model specication test of a parametric model against a nonparametric or semiparametric alt...
This paper considers a nonparametric time series regression model with a nonstationary regressor. We...
We propose a test for model specification of a parametric diffusion process based on a kernel estima...
We propose a test for model specification of a parametric diffusion process based on a kernel estima...
Although estimation and testing are different statistical problems, if we want to use a test statist...
© Institute of Mathematical Statistics, 2009This paper considers a class of nonparametric autoregres...
AbstractThis paper investigates performance of nonparametric kernel regression and its associated ba...
Semiparametric and nonparametric estimators are becoming indispensable tools in applied econometric...
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
We examine a consistent test for the correct specification of a regression function with dependent d...
The unknown error density of a nonparametric regression model is approximated by a mixture of Gaussi...