Fast Kernel Smoothing in R with Applications to Projection Pursuit [PDF]
Journal of Statistical Software, 2022 This paper introduces the R package FKSUM, which offers fast and exact evaluation of univariate kernel smoothers. The main kernel computations are implemented in C++, and are wrapped in simple, intuitive and versatile R functions.
David P. Hofmeyr
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Discrete Heat Kernel Smoothing in Irregular Image Domains. [PDF]
Annu Int Conf IEEE Eng Med Biol Soc, 2018 We present the discrete version of heat kernel smoothing on graph data structure. The method is used to smooth data in an irregularly shaped domains in 3D images. New statistical properties of heat kernel smoothing are derived. As an application, we show
Chung MK, Wang Y, Wu G.
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Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing [PDF]
International Conference on Learning Representations, 2023 Calibration measures and reliability diagrams are two fundamental tools for measuring and interpreting the calibration of probabilistic predictors. Calibration measures quantify the degree of miscalibration, and reliability diagrams visualize the structure of this miscalibration.
Błasiok, Jarosław, Nakkiran, Preetum
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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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KVLMM: A Trajectory Prediction Method Based on a Variable-Order Markov Model With Kernel Smoothing
IEEE Access, 2018 With the dramatic proliferation of global positioning system (GPS) devices, a rich range of research has been conducted on the analysis of GPS trajectories.
Xing Wang +3 more
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Integral approximation by kernel smoothing [PDF]
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.
P. Speckman
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Heat kernel smoothing using Laplace-Beltrami eigenfunctions. [PDF]
Med Image Comput Comput Assist Interv, 2010 We present a novel surface smoothing framework using the Laplace-Beltrami eigenfunctions. The Green's function of an isotropic diffusion equation on a manifold is constructed as a linear combination of the Laplace-Beltraimi operator. The Green's function is then used in constructing heat kernel smoothing.
Seo S, Chung MK, Vorperian HK.
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Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and Epanechnikov Kernels. [PDF]
Appl Psychol Meas, 2015 Cid JA, von Davier AA.
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Estimating Mixture of Gaussian Processes by Kernel Smoothing. [PDF]
J Bus Econ Stat, 2014 Huang M, Li R, Wang H, Yao W.
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