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Bandwidth selection in smoothing functions
East African Journal of Statistics, 2007A simple criterion for selecting a bandwidth parameter that controls the amount of smoothing in functions is described. The procedure is computationally inexpensive and, hence, worth adopting. We argue that the bandwidth parameter is determined by two factors: the kernel function and the length of the smoothing region.
Kibua, T K, Karuku, M
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Smoothing bandwidth selection for response latency estimation
Journal of Neuroscience Methods, 1999Stimulus response latency is the delay between stimulus onset and the evoked modulation in neural activity. A common technique to estimate latencies involves binning the spike arrival times to form a peri-stimulus histogram. This histogram is smoothed using a fixed bandwidth. The estimated latency is the first time following stimulus onset in which the
H S, Friedman, C E, Priebe
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Visual Bandwidth Selection for Kernel Density Maps
Photogrammetrie - Fernerkundung - Geoinformation, 2009Within this paper we investigate the challenge to find an appropriate bandwidth in kernel density estimation. Kernel density estimation methods can be used in visualizing and analyzing spatial data, with the objective of understanding and potentially predicting event patterns.
Krisp, Jukka M. +3 more
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1991
For various data-based bandwidth selectors for a kernel density estimator, the relative rate of convergence of the selected bandwidth is considered. Several methods have recently been found which have the very fast rate of convergence of the square root of the sample size.
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For various data-based bandwidth selectors for a kernel density estimator, the relative rate of convergence of the selected bandwidth is considered. Several methods have recently been found which have the very fast rate of convergence of the square root of the sample size.
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Bandwidth Selection for Kernel Estimates
2001This chapter is about the choice of the bandwidth (or smoothing factor) h ∈ (0, ∞) of the standard kernel estimate $$ {f_{n,h}}(x) = \frac{1}{{n{h^d}}}\sum\limits_{i = 1}^n {K\left( {\frac{{x - {X_i}}}{h}} \right)} . $$
Luc Devroye, Gábor Lugosi
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Bandwidth Selection in Practice
1991The choice of the bandwidth h is the main problem of kernel density estimation. In some situations it might be quite useful to have a set of estimates corresponding to different bandwidths. Those estimates can highlight different aspects in the structure of the data. However, the presentation and iterpretation of such curves is quite subjective.
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Optimal bandwidth selection for MLS surfaces
2008 IEEE International Conference on Shape Modeling and Applications, 2008We address the problem of bandwidth selection in MLS surfaces. While the problem has received relatively little attention in the literature, we show that appropriate selection plays a critical role in the quality of reconstructed surfaces. We formulate the MLS polynomial fitting step as a kernel regression problem for both noiseless and noisy data ...
null Hao Wang +2 more
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Selection of bandwidth for kernel regression
Communications in Statistics - Theory and Methods, 2016The most important factor in kernel regression is a choice of a bandwidth. Considerable attention has been paid to extension the idea of an iterative method known for a kernel density estimate to kernel regression. Data-driven selectors of the bandwidth for kernel regression are considered. The proposed method is based on an optimally balanced relation
Jan Koláček, Ivanka Horová
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