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A note on the behaviour of a kernel-smoothed kernel density estimator
Statistics & Probability Letters, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Janssen, Paul +2 more
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Kernel smoothing for finite populations
Statistics and Computing, 1993We 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 +2 more
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Evolving Smoothing Kernels for Global Optimization
2016The 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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Fast Kernel Smoothing by a Low-Rank Approximation of the Kernel Toeplitz Matrix
Journal of Mathematical Imaging and Vision, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guang Deng +2 more
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Entropic kernels for data smoothing
Statistics & Probability Letters, 2013Abstract Data smoothing or regression kernels based on locational entropy embody the principle that observations towards the extremes of the chosen data window should provide less information than those at the midpoint. Weight patterns can be flexible, depending on the choice of prior information density.
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Singular numbers of smooth kernels
Mathematical Proceedings of the Cambridge Philosophical Society, 1988In [12] we elaborate the vague principle that the behaviour at infinity of the decreasing sequence of singular numbers sn(K) of a Hilbert–Schmidt kernel K is at least as good as that of the sequence {n−1/qω(n−1;K)}, where ωp is an Lp-modulus of continuity of K and q = p/(p − 1), where 1 ≤ p ≤ 2.
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Deconvolution with arbitrarily smooth kernels
Statistics & Probability Letters, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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