Results 221 to 230 of about 48,941 (244)

OPTIMAL BANDWIDTH SELECTION IN NONLINEAR COINTEGRATING REGRESSION

Econometric Theory, 2020
We study optimal bandwidth selection in nonparametric cointegrating regression where the regressor is a stochastic trend process driven by short or long memory innovations. Unlike stationary regression, the optimal bandwidth is found to be a random sequence which depends on the sojourn time of the process. All random sequences $h_{n}$ that lie within
Wang, Qiying, Phillips, Peter C. B.
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Bandwidth selection in robust smoothing

Journal of Nonparametric Statistics, 1993
In robust smoothing of regression functions of the type , the theory in automatic bandwidth selection is still lacking. This paper tries to fill the gap by looking at the use of cross-validation methods in robust smoothing. Conjectures regarding the use of a class of robust cross-validation method are made.
Leung, Denis H. Y., Marriott, F. H. C., Wu, E. K. H.   +2 more
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Bandwidth selection in smoothing functions

East African Journal of Statistics, 2007
A 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, 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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Visual Bandwidth Selection for Kernel Density Maps

Photogrammetrie - Fernerkundung - Geoinformation, 2009
Within 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., Peters, Stefan, Murphy, Christian E., Fan, Hongchao   +3 more
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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 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 Practice

1991
The 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, 2008
We 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, Carlos E. Scheidegger, Claudio T. Silva   +2 more
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Selection of bandwidth for kernel regression

Communications in Statistics - Theory and Methods, 2016
The 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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