Bootstrap Bandwidth Selection in Kernel Density Estimation from a Contaminated Sample*
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چکیده
منابع مشابه
Practical bandwidth selection in deconvolution kernel density estimation
Kernel estimation of a density based on contaminated data is considered and the important issue of how to choose the bandwidth parameter in practice is discussed. Some plug-in (PI) type of bandwidth selectors, which are based on non-parametric estimation of an approximation of the mean integrated squared error, are proposed. The selectors are a re4nement of the simple normal reference bandwidth...
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Various bootstrap methodologies are discussed for the selection of the bandwidth of a kernel density estimator. The smoothed bootstrap is seen to provide new and independent motivation of some previously proposed methods. A curious feature of bootstrapping in this context is that no simulated resampling is required, since the needed functionals of the distribution can be calculated explicitly.
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The error distribution is generally unknown in deconvolution problems with real applications. A separate independent experiment is thus often conducted to collect the additional noise data in those studies. In this paper, we study the nonparametric deconvolution estimation from a contaminated sample coupled with an additional noise sample. A ridge-based kernel deconvolution estimator is propose...
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Allthough nonparametric kernel density estimation is nowadays a standard technique in explorative data{analysis, there is still a big dispute on how to assess the quality of the estimate and which choice of bandwidth is optimal. The main argument is on whether one should use the Integrated Squared Error or the Mean Integrated Squared Error to deene the optimal bandwidth. In the last years a lot...
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