Results 221 to 230 of about 41,711 (262)

KERNEL REGRESSION SMOOTHING OF TIME SERIES

Journal of Time Series Analysis, 1992
Abstract. A class of non‐parametric regression smoothers for times series is defined by the kernel method. The kernel approach allows flexible modelling of a time series without reference to a specific parametric class. The technique is applicable to detection of non‐linear dependences in time series and to prediction in smooth regression models with ...
Härdle, Wolfgang, Vieu, Philippe
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Smoothing for Discrete Kernels in Discrimination

Biometrical Journal, 1988
AbstractIn multivariate discrimination by the discrete kernel method the allocation rule is Bayes risk consistent if the smoothing parameter is chosen by maximization of the leaving‐one‐out nonerror rate. It is shown that consistency still holds if the leaving‐one‐out nonerror rate is replaced by a smoothed version. Thus a cross‐validatory criterion is
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Kernel smoothing for jagged edge reduction

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
In this paper, we consider the problem of removing jaggy artifacts from images. We consider the kernel regression framework and propose a reduced-rank quadratic adaptive method that adapts to the local gradient direction. The proposed technique is effective in shrinking isophote fluctuations, and the result is smooth edges.
Mohammad Aghagolzadeh, Andrew Segall
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Eigenvalues of smooth kernels

Mathematical Proceedings of the Cambridge Philosophical Society, 1984
Suppose is a symmetric square integrable kernel on the unit square [0, 1]2. Thenis a compact symmetric operator on the Hilbert space L2[0, 1]. H. Weyl (see [2]) has shown that, if then the eigenvaluesof T satisfy as n → ∞. We prove a related result.
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Fast Kernel Smoothing by a Low-Rank Approximation of the Kernel Toeplitz Matrix

Journal of Mathematical Imaging and Vision, 2018
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Guang Deng, Jonathan H. Manton, Song Wang 0003   +2 more
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A note on the behaviour of a kernel-smoothed kernel density estimator

Statistics & Probability Letters, 2020
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Janssen, Paul, Swanepoel, Jan, Veraverbeke, Noël   +2 more
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Evolving Smoothing Kernels for Global Optimization

2016
The Diffusion-Equation Method (DEM) – sometimes synonymously called the Continuation Method – is a well-known natural computation approach in optimization. The DEM continuously transforms the objective function by a (Gaussian) kernel technique to reduce barriers separating local and global minima.
Paul Manns, Kay Hamacher
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Kernel smoothing for finite populations

Statistics and Computing, 1993
We identify a role for smooth curve provision in the finite population context. The performance of kernel density estimates in this scenario is explored, and they are tailored to the finite population situation especially by developing a method of data-based selection of the smoothing parameter appropriate to this problem. Simulated examples are given,
M. C. Jones, I. S. Bradbury
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Adapting kernel estimation to uncertain smoothness [PDF]

, 2011
For local and average kernel based estimators, smoothness conditions ensure that the kernel order determines the rate at which the bias of the estimator goes to zero and thus allows the econometrician to control the rate of convergence. In practice, even with smoothness the estimation errors may be substantial and sensitive to the choice of the ...
Yulia Kotlyarova, Marcia M Schafgans, Victoria Zinde-Walsh   +2 more
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Kernel Smoothing

Technometrics, 1996
Brian S. Yandell, M. P. Wand, M. C. Jones   +2 more
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