Results 261 to 270 of about 2,588,636 (293)

Bandwidth Selection for Kernel Estimates

2001
This 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 KERNEL SMOOTHING OF TIME SERIES

Journal of Time Series Analysis, 1996
Abstract. The kernel smoothing method has been considered as a useful tool for identification and prediction in time series models. In practice this method is to be tuned by a smoothing parameter. For selection of the smoothing parameter, Härdle and Vieu (Kernel regression smoothing of time series. J. Time Ser. Anal.
Kim, Tae Yoon, Cox, Dennis D.
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Wavelength selective switching for optical bandwidth management

Bell Labs Technical Journal, 2006
Optical transport capacities have grown significantly in the last decade to meet the increased demands on communications networks. This growth has been achieved both by increases in individual channel rates, which are based on time division multiplexing (TDM), and by increased channel counts, through the use of dense wavelength division multiplexing ...
David T. Neilson, Christopher R. Doerr, Dan M. Marom, Roland Ryf, Mark P. Earnshaw   +4 more
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On the Selection of the Bandwidth Parameter for the k‐Chart

Quality and Reliability Engineering International, 2017
The k‐chart, based on support vector data description, has received recent attention in the literature. We review four different methods for choosing the bandwidth parameter, s, when the k‐chart is designed using the Gaussian kernel. We provide results of extensive Phase I and Phase II simulation studies varying the method of choosing the bandwidth ...
Maria L. Weese, Waldyn G. Martinez, L. Allison Jones-Farmer   +2 more
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Smoothing bandwidth selection for response latency estimation

Journal of Neuroscience Methods, 1999
Stimulus 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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Bandwidth reservation with selectable bit-rate streams

Multimedia Systems, 1998
Video and audio compression techniques allow continuous media streams to be transmitted at bit rates that are a function of the delivered quality of service. Digital networks will be increasingly used for the transmission of such continuous media streams.
Carl J. Beckmann, Ahmed A. Moin, Saurab Nog   +2 more
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Bandwidth Selection in Density Estimation

1995
The motivation for density estimation in statistics and data analysis is to realize where observations occur more frequently in a sample. The aim of density estimation is to approximate a “true” probability density function f(x) from a sample information {X i }n i=1 of independent and identically distributed observations.
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Bayesian sampling for bandwidth selection

2017
This thesis aims to investigate three main topics, which are the density-based multiple variance ratio test, Bayesian estimation of partially linear models, and Bayesian estimation of Tobit models. All three topics involve a kernel density estimator whose performance is mainly determined by the choice of bandwidth.
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Root N Bandwidth Selection

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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Bandwidth selection for the smoothed bootstrap percentile method

Computational Statistics & Data Analysis, 2001
Some applications of the bootstrap involve smoothing the estimated distribution that is resampled, a method known as the smoothed bootstrap. Recently, the effect of resampling a kernel smoothed distribution was evaluated through expansions for the coverage of bootstrap percentile confidence intervals.
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