We investigate an automatic method of determining a local bandwidth for non-parametric kernel spectral density estimates at a single frequency. This procedure is a modification of a cross-validation technique for global bandwidth choices, avoiding the computation of any pilot estimate based on initial bandwidths or on approximate parametric models. Only local conditions on the spectral density around the frequency of interest are assumed. We illustrate with a Monte Carlo study the performance in finite samples of the bandwidth estimates proposed.Publicad
We review the extensive recent literature on automatic, data-based selection of a global smoothing p...
In the present paper we combine the issues of bandwidth choice and construction of confidence interv...
Variable bandwidth kernel density estimators increase the window width at low densities and decrease...
We investigate an automatic method of determining a local bandwidth for non-parametric kernel spectr...
We investigate an automatic method of determining a local bandwidth for nonparametric kernel spectra...
Abstract. This article gives ideas for developing statistics software which can work without user in...
We consider the problem of bandwidth selection by cross-validation from a sequential point of view i...
Abstract: This paper deals with optimal window width choice in non-parametric lag- or spectral windo...
The performance of multivariate kernel density estimates depends crucially on the choice of bandwidt...
Nonparametric kernel density estimation method makes no assumptions on the functional form of the cu...
AbstractCross-validation methodologies have been widely used as a means of selecting tuning paramete...
AbstractThis paper studies the risks and bandwidth choices of a kernel estimate of the underlying de...
Abstract: Local linear kernel methods have been shown to dominate local constant methods for the non...
In the context of nonparametric regression estimation, the behaviour of kernel methods such as the N...
Most recently proposed bandwidth selectors in kernel density estimation have been developed with int...
We review the extensive recent literature on automatic, data-based selection of a global smoothing p...
In the present paper we combine the issues of bandwidth choice and construction of confidence interv...
Variable bandwidth kernel density estimators increase the window width at low densities and decrease...
We investigate an automatic method of determining a local bandwidth for non-parametric kernel spectr...
We investigate an automatic method of determining a local bandwidth for nonparametric kernel spectra...
Abstract. This article gives ideas for developing statistics software which can work without user in...
We consider the problem of bandwidth selection by cross-validation from a sequential point of view i...
Abstract: This paper deals with optimal window width choice in non-parametric lag- or spectral windo...
The performance of multivariate kernel density estimates depends crucially on the choice of bandwidt...
Nonparametric kernel density estimation method makes no assumptions on the functional form of the cu...
AbstractCross-validation methodologies have been widely used as a means of selecting tuning paramete...
AbstractThis paper studies the risks and bandwidth choices of a kernel estimate of the underlying de...
Abstract: Local linear kernel methods have been shown to dominate local constant methods for the non...
In the context of nonparametric regression estimation, the behaviour of kernel methods such as the N...
Most recently proposed bandwidth selectors in kernel density estimation have been developed with int...
We review the extensive recent literature on automatic, data-based selection of a global smoothing p...
In the present paper we combine the issues of bandwidth choice and construction of confidence interv...
Variable bandwidth kernel density estimators increase the window width at low densities and decrease...