Results 11 to 20 of about 165,608 (287)
Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images. [PDF]
Med Image Anal, 2015 We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a ...
Chung MK, Qiu A, Seo S, Vorperian HK.
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ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R [PDF]
Journal of Statistical Software, 2007 Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing.
Tarn Duong
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Integral approximation by kernel smoothing
Bernoulli, 2016 Let $(X_1,\ldots,X_n)$ be an i.i.d. sequence of random variables in $\mathbb{R}^d$, $d\geq 1$. We show that, for any function $ :\mathbb{R}^d\rightarrow\mathbb{R}$, under regularity conditions, \[n^ {1/2}\Biggl(n^{-1}\sum_{i=1}^n\frac{ (X_i)}{\widehat{f}^(X_i)}- \int (x)\,dx\Biggr)\stackrel{\mathbb{P}}{\longrightarrow}0,\] where $\widehat{f}$ is ...
Delyon, Bernard, Portier, François
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Kernel Smoothing in Partial Linear Models
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1988 SUMMARY Kernel smoothing is studied in partial linear models, i.e. semiparametric models of the form yi=ξi′β+f(ti)+εi(1⩽i⩽n), where the ξi are fixed known p vectors, β is an unknown vector parameter and f is a smooth but unknown function.
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Yield Curve Estimation by Kernel Smoothing Methods [PDF]
SSRN Electronic Journal, 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Linton, O. +3 more
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Kernel Smoothing of Data with Correlated Errors [PDF]
Journal of the American Statistical Association, 1990 Abstract Kernel smoothing is a common method of estimating the mean function in the nonparametric regression model y = f(x) + e, where f(x) is a smooth deterministic mean function and e is an error process with mean zero. In this article, the mean squared error of kernel estimators is computed for processes with correlated errors, and the estimators ...
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Acta Universitatis Lodziensis. Folia Oeconomica, 2017 Ad hoc methods in the choice of smoothing parameter in kernel density estimation, although often used in practice due to their simplicity and hence the calculated efficiency, are characterized by quite big error.
Aleksandra Katarzyna Baszczyńska
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Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications
Algorithms, 2022 We propose an alternative maximum entropy approach to learning the spectra of massive graphs. In contrast to state-of-the-art Lanczos algorithm for spectral density estimation and applications thereof, our approach does not require kernel smoothing.
Diego Granziol +5 more
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Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2004 Kernel smoothing provides a simple way for finding structure in data. The idea of the kernel smoothing can be applied to a simple fixed design regression model and a random design regression model.
Jitka Poměnková
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Adversarially Robust Kernel Smoothing
, 2021 We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization. Our method is motivated by the convex analysis perspective of distributionally robust optimization based on probability metrics, such as the Wasserstein distance and the maximum mean discrepancy.
Zhu, Jia-Jie +3 more
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