Journal ArticleDOI
A stabilized bandwidth selection method for kernel smoothing of the periodogram
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TLDR
A new bandwidth selection method that is based on a coupling of the so-called plug-in and the unbiased risk estimation ideas is proposed, which often outperforms some other commonly used bandwidth selection methods.About:
This article is published in Signal Processing.The article was published on 2001-02-01. It has received 31 citations till now. The article focuses on the topics: Smoothing & Bandwidth (signal processing).read more
Citations
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Journal ArticleDOI
Pulmonary vein region ablation in experimental vagal atrial fibrillation: role of pulmonary veins versus autonomic ganglia.
Kristina Lemola,Denis Chartier,Yung-Hsin Yeh,Marc Dubuc,Raymond Cartier,Andrew Armour,Michael Ting,Masao Sakabe,Akiko Shiroshita-Takeshita,Philippe Comtois,Stanley Nattel +10 more
TL;DR: Intact PVs are not needed for maintenance of experimental cholinergic AF and ablation of the autonomic ganglia at the base of the PVs suppresses vagal responses and may contribute to the effectiveness of PV-directed ablation procedures in vagal AF.
Journal ArticleDOI
Smoothing parameter selection for smoothing splines: a simulation study
TL;DR: A simulation study of several smoothing parameter selection methods, including two so-called risk estimation methods, finds that the popular method, generalized cross-validation, was outperformed by another method, an improved Akaike Information criterion, that shares the same assumptions and computational complexity.
Journal ArticleDOI
Effects of Two Different Catheter Ablation Techniques on Spectral Characteristics of Atrial Fibrillation
Kristina Lemola,Michael Ting,Priya Gupta,Jeffrey N. Anker,Aman Chugh,Eric Good,Scott Reich,David Tschopp,Petar Igic,Darryl Elmouchi,Krit Jongnarangsin,Frank Bogun,Frank Pelosi,Fred Morady,Hakan Oral +14 more
TL;DR: Both CPVA and EGA decrease the DF of AF, consistent with elimination of high-frequency sources, which suggests that CPVA or EGA eliminate different mechanisms in the genesis of persistent AF.
Journal ArticleDOI
The generalized shrinkage estimator for the analysis of functional connectivity of brain signals
Mark Fiecas,Hernando Ombao +1 more
TL;DR: In this article, the generalized shrinkage estimator is proposed for estimating functional connectivity between neurophysiological signals represented by a multivariate time series, where the optimal weights are frequency-specific and derived under the quadratic risk criterion so that the estimator that performs better at a particular frequency receives heavier weight.
Journal ArticleDOI
Testing nonparametric and semiparametric hypotheses in vector stationary processes
TL;DR: In this article, a nonparametric approach for testing hypotheses about the spectral density matrix of multivariate stationary time series based on estimating the integrated deviation from the null hypothesis is proposed.
References
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BookDOI
Density estimation for statistics and data analysis
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Journal ArticleDOI
Estimation of the Mean of a Multivariate Normal Distribution
TL;DR: In this article, an unbiased estimate of risk is obtained for an arbitrary estimate, and certain special classes of estimates are then discussed, such as smoothing by using moving averages and trimmed analogs of the James-Stein estimate.
Journal ArticleDOI
Kernels for Nonparametric Curve Estimation
TL;DR: In this article, the choice of kernels for nonparametric estimation of regression functions and their derivatives is investigated, and explicit expressions are obtained for kernels minimizing the asymptotic variance or the IMSE (the present proof of the optimality of the latter kernels up to order k = 5).
Journal ArticleDOI
How Far are Automatically Chosen Regression Smoothing Parameters from their Optimum
TL;DR: In this paper, the problem of smoothing parameter selection for nonparametric curve estimators in the specific context of kernel regression estimation is addressed, and the convergence rate turns out to be excruciatingly slow.