Results 311 to 320 of about 1,593,883 (356)

Kernel Smoothed Consumption-Age Quantiles

The Canadian Journal of Economics, 1992
In earlier work, the authors explored life-cycle consumption profiles of Canadian married couple families. That research concluded that the common presumption in simulation models of upward-sloping consumption-age profiles accompanied by dissaving in retirement could not be supported in Canadian data.
A.L. Robb, L. Magee, J.B. Burbidge
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On the Smoothness of General Kernels

Canadian Journal of Mathematics, 1966
In (3, §2), the writer and F. E. Browder stated briefly, without proof, some results concerning general distribution kernels. It is our aim here to prove and complete those results.The terminology and notations are introduced in §1.In §2 we define the notion of domain of dependence with respect to the kernel Kx,y (Definition 1) as well as the notion of
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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

Technometrics, 1996
Brian S. Yandell, M. P. Wand, M. C. Jones   +2 more
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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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Deconvolution with arbitrarily smooth kernels

Statistics & Probability Letters, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Kernel Smoothing in Quantal Bioassay

Japanese journal of applied statistics, 2003
A nonparametric method for the estimation of effective doses by kernel smoothing is proposed. The estimator of the dose and its asymptotic confidence interval are given. The estimation is based on the asymptotic properties of the proposed kernel estimator of dose response curves.
Hidenori Okumura, Kanta Naito
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Singular numbers of smooth kernels

Mathematical Proceedings of the Cambridge Philosophical Society, 1988
In [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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Entropic kernels for data smoothing

Statistics & Probability Letters, 2013
Abstract 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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Adaptive kernel smoothing regression using vector quantization

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS), 2011
A method for performing kernel smoothing regression in an online adaptive manner is presented. The approach proposed is to apply kernel smoothing regression on an incremental estimation of the (evolving) probability distribution of the incoming data stream rather than the sequence of observations.
Federico Montesino Pouzols, Amaury Lendasse   +1 more
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