Add to ORKGAbstract: This paper provides an asymptotic expansion for the mean integrated squared error (MISE) of nonlinear wavelet-based mean regression function estimators with long memory data. This MISE expansion, when the underlying mean regression function is only piecewise smooth, is the same as analogous expansion for the kernel estimators. However, for the kernel estimators, this MISE expansion generally fails if the additional smoothness assumption is absent. Key words: nonlinear wavelet-based estimator; nonparametric regression; long-range dependenc
International audienceLong-memory noise is common to many areas of signal processing and can serious...
We show that a nonparametric estimator of a regression function, obtained as solution of a specific ...
We attempt to recover a regression function from noisy data. It is assumed that the underlying funct...
Two wavelet based estimators are considered in this paper for the two parameters that characterize l...
In this thesis, we investigate some adaptive wavelet approaches for a so-called nonparametric regres...
In this article we study function estimation via wavelet shrinkage for data with long-range dependen...
We consider the estimation of nonparametric regression function with long memory data and investigat...
Summary. We present and study the performance of the semiparametric wavelet estimator for the long{m...
We consider asymptotic expansion of the nonparametric M-estimator in a fixed-design nonlinear regres...
This paper studies the estimation of time series regression when both regressors and disturbances ha...
In this paper, we propose the wavelet estimation of the regression function in certain kind of nonli...
International audienceIn this paper we provide a theoretical contribution to the point-wise mean squ...
The nonlinear wavelet estimator of regression function with random design is constructed. The optima...
Abstract: We investigate global performances of non-linear wavelet estimation in regression models w...
International audienceWe consider the estimation of an unknown regression function from a nonparamet...
International audienceLong-memory noise is common to many areas of signal processing and can serious...
We show that a nonparametric estimator of a regression function, obtained as solution of a specific ...
We attempt to recover a regression function from noisy data. It is assumed that the underlying funct...
Two wavelet based estimators are considered in this paper for the two parameters that characterize l...
In this thesis, we investigate some adaptive wavelet approaches for a so-called nonparametric regres...
In this article we study function estimation via wavelet shrinkage for data with long-range dependen...
We consider the estimation of nonparametric regression function with long memory data and investigat...
Summary. We present and study the performance of the semiparametric wavelet estimator for the long{m...
We consider asymptotic expansion of the nonparametric M-estimator in a fixed-design nonlinear regres...
This paper studies the estimation of time series regression when both regressors and disturbances ha...
In this paper, we propose the wavelet estimation of the regression function in certain kind of nonli...
International audienceIn this paper we provide a theoretical contribution to the point-wise mean squ...
The nonlinear wavelet estimator of regression function with random design is constructed. The optima...
Abstract: We investigate global performances of non-linear wavelet estimation in regression models w...
International audienceWe consider the estimation of an unknown regression function from a nonparamet...
International audienceLong-memory noise is common to many areas of signal processing and can serious...
We show that a nonparametric estimator of a regression function, obtained as solution of a specific ...
We attempt to recover a regression function from noisy data. It is assumed that the underlying funct...