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An Efficient Algorithm for the Least-Squares Cross-Validation with Symmetric and Polynomial Kernels

Byung Gook Lee, Byung Chun Kim
1990 Communications in statistics. Simulation and computation  
In order to compute score function Mo, Silverman (1982) presents an algorithm for the efficient computation of kernel density estimate method by Fourier transform method and the improvements to this  ...  But the direct use for computation of for- mular (3) for the kernel estimate is highly inefficient because it appears that n(n -1)/2 evaluations of the function and K are needed to compute each  ... 
doi:10.1080/03610919008812933 fatcat:bmu7ikskrngrzfc7ol4ck2yod4

Efficient Density Estimation and Value at Risk Using Fejér-Type Kernel Functions

Olga Kosta, Natalia Stepanova
2015 Journal of Mathematical Finance  
This paper presents a nonparametric method for computing the Value at Risk (VaR) based on efficient density estimators with Fejér-type kernel functions and empirical bandwidths obtained from Fourier analysis  ...  The kernel-type estimator with a Fejér-type kernel was recently found to dominate all other known density estimators under the p  -risk, p 1 ≤ < ∞ .  ...  is used for density estimators with efficient kernels.  ... 
doi:10.4236/jmf.2015.55040 fatcat:t3vmksphdbd3hemwzswbjxd3gm

Efficient Edge Detection Using Mean Shift Smoothing and Gradient-Based Noise Removal

Haitao Sang, Zhen Zhou
2016 Innovative Computing Information and Control Express Letters, Part B: Applications  
For the low-contrast edge and slow calculation speed of conventional algorithms for edge detection, this paper proposes an efficient method of edge detection using mean shift smoothing and gradient-based  ...  The mean shift smoothing could efficiently protect the edge details and amplify the contrast along image edges to give more clear contours in edge detection.  ...  Density estimate function for a random data set Algorithm 1 1 : 2 × 1 255 ; 2 : 3 : 11223 Smoothing algorithm based on mean shift procedure Evaluate the normalization c = height+width Initialize data  ... 
doi:10.24507/icicelb.07.03.519 fatcat:ydmkuvoaafelxcr2fqotto2nki

Statistically Efficient Estimation for Non-Smooth Probability Densities

Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida
2018 International Conference on Artificial Intelligence and Statistics  
We investigate statistical efficiency of estimators for non-smooth density functions.  ...  Although estimators and their convergence rates for smooth density functions are well investigated in the literature, those for non-smooth density functions remain elusive despite their importance in application  ...  Nevertheless, statistically efficient estimation of non-smooth density functions has been elusive.  ... 
dblp:conf/aistats/ImaizumiMY18 fatcat:m4wex7eoffcr3jgluupxxesmeu

Improvements on Lightning Density Estimation Based on Analysis of Lightning Location System Performance Parameters: Brazilian Case

Vandoir Bourscheidt, Osmar Pinto, Kleber P. Naccarato
2014 IEEE Transactions on Geoscience and Remote Sensing  
Index Terms- Detection efficiency (DE) models, kernel smoothness, lightning density maps, lightning location systems (LLS), location accuracy (LA), network performance.  ...  Two parameters are typically used to evaluate system performance: detection efficiency (DE) and location accuracy (LA).  ...  ACKNOWLEDGMENTS The authors would like to thank ELETROBRAS FURNAS for the transmission line georeferenced data and for logistical support regarding the DE model. Additionally, Ken Cummins  ... 
doi:10.1109/tgrs.2013.2253109 fatcat:xxvr2fppsbf27ikhopeaekjwau

Resource-aware kernel density estimators over streaming data

Christoph Heinz, Bernhard Seeger
2006 Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06  
A convenient method for density estimation utilizes kernels. However, its computational complexity collides with the rigid processing requirements of data streams.  ...  With respect to these requirements, we specifically aim to provide kernel density estimators over data streams.  ...  an efficient evaluation of M-Kernels. • There is no theoretical foundation for the proposed bandwidth computation.  ... 
doi:10.1145/1183614.1183772 dblp:conf/cikm/HeinzS06 fatcat:kose47v4jfbdtghincdne7obka

A note non Nonparametric Regression Modeling using a Density Function

Jakperik Dioggban
2020 African Journal of Applied Statistics  
In recent years, It has been applied using Kernel estimators and the smoothing splines, but these methods wields some bias of estimation.  ...  Simulation studies conducted showed that the proposed estimator performs better than both the Kernel and the smoothing splines estimators especially with large samples  ...  The proposed estimator does not offer extreme efficiency for small sample sizes, thus its performance was notably close to that of the smoothing splines estimator.  ... 
doi:10.16929/ajas/2020.993.252 fatcat:vrypx7nizneahgvk5m2eahyhyi

Smoothed Particles: A new paradigm for animating highly deformable bodies [chapter]

Mathieu Desbrun, Marie-Paule Gascuel
1996 Eurographics  
We show that the smoothed particles paradigm leads to a coherent definition of the object's surface as an iso-surface of the mass density function.  ...  This paper presents a new formalism for simulating highly deformable bodies with a particle system.  ...  Aknowledgements The authors would like to thank Eugene Fiume for pointing us to SPH and for early discussions, Andy Hanson for his continuous help, and François Faure and Rémi Cozot for a full re-reading  ... 
doi:10.1007/978-3-7091-7486-9_5 fatcat:qqpmu4ytnvgyjfzdobfgkguggu

Linear-scaling electronic structure theory: Electronic temperature in the Kernel Polynomial Method [article]

Eunan J. McEniry, Ralf Drautz
2017 arXiv   pre-print
Linear-scaling electronic structure methods based on the calculation of moments of the underlying electronic Hamiltonian offer a computationally efficient and numerically robust scheme to drive large-scale  ...  We show that the convolution of the kernel polynomial method may be understood as an effective electron temperature.  ...  Electronic densities of states for diamond and graphene evaluated for the various kernels, with N max = 40.  ... 
arXiv:1701.01568v1 fatcat:yatjgyqajfhfpkbvwvtf37y6rq

A new family of kernels from the beta polynomial kernels with applications in density estimation

Israel Uzuazor Siloko, Wilson Nwankwo, Edith Akpevwe Siloko
2020 IJAIN (International Journal of Advances in Intelligent Informatics)  
Hence, more kernel functions with smoothing parameter selectors have been propounded customarily in density estimation.  ...  The newly proposed kernel family was evaluated with simulated and life data.  ...  No additional information is available for this paper.  ... 
doi:10.26555/ijain.v6i3.456 fatcat:alb5nygqnjgfrn3y7dovcw2tdu

A Computationally Efficient Multivariate Maximum-Entropy Density Estimation (MEDE) Technique

Y. Kouskoulas, L.E. Pierce, F.T. Ulaby
2004 IEEE Transactions on Geoscience and Remote Sensing  
Density estimation is the process of taking a set of multivariate data and finding an estimate for the probability density function (pdf) that produced it.  ...  One approach for obtaining an accurate estimate of the true density ( ) is to use the polynomial-moment method with Boltzmann-Shannon entropy.  ...  We will compare the maximum-entropy density estimate with the kernel density estimate in terms of computational efficiency by evaluating the density at a particular point.  ... 
doi:10.1109/tgrs.2003.821068 fatcat:yk6j4ha3gbbl5cm4badjlcdbca

Numerical Modeling for Discrete Multibody Interaction and Multifeild Coupling Dynamics Using the SPH Method

Azhar Halik, Rahmatjan Imin, Mamtimin Geni, Afang Jin, Yangyang Mou
2015 Mathematical Problems in Engineering  
Each particle defines a particular kernel smoothing length such as larger smoothing length which is used to calculate continuous homogenous body.  ...  Some characteristics of the SPM and MPM are evaluated.  ...  The new SPH kernel smoothing length is represented and modeled for evaluating the multibody correct contact and multiphase interaction problems.  ... 
doi:10.1155/2015/205976 fatcat:4mellewahvfu5ot4gdkbcrzjb4

Fast Exact Univariate Kernel Density Estimation [article]

David P. Hofmeyr
2018 arXiv   pre-print
This paper presents new methodology for computationally efficient kernel density estimation.  ...  It is shown that a large class of kernels allows for exact evaluation of the density estimates using simple recursions.  ...  Computing Kernel Density Estimates Exactly This section is concerned with efficient evaluation of univariate kernel density estimates.  ... 
arXiv:1806.00690v2 fatcat:4qy6oenblndflgibnp2s4hmqme

Accelerating small-angle scattering experiments on anisotropic samples using kernel density estimation

Kotaro Saito, Masao Yano, Hideitsu Hino, Tetsuya Shoji, Akinori Asahara, Hidekazu Morita, Chiharu Mitsumata, Joachim Kohlbrecher, Kanta Ono
2019 Scientific Reports  
We applied kernel density estimation to the average of a hundred short scans and evaluated noise reduction effects of kernel density estimation (smoothing).  ...  Although there is no advantage of using smoothing for isotropic data due to the powerful noise reduction effect of radial averaging, smoothing with a statistically and physically appropriate kernel can  ...  Tetsuro Ueno for fruitful discussion and his comments on the manuscript and Dr. Jonathan White for proofreading and his advice to include supplementary materials.  ... 
doi:10.1038/s41598-018-37345-5 fatcat:f6dr2dwx4vd4pixfq2gw7tfvvy

Adaptive smoothing length method based on weighted average of neighboring particle density for SPH fluid simulation

Rongda Zeng, Zihao Wu, Shengbang Deng, Jian Zhu, Xiaoyu Chi
2021 Virtual Reality & Intelligent Hardware  
In this method, the smoothing length is adaptively adjusted according to the ratio of the particle density to the weighted average of the density of the neighboring particles.  ...  Additionally, a neighbor search scheme and kernel function scheme are designed to solve the asymmetry problems caused by the variable smoothing length.  ...  Density-based adaptive smoothing length To obtain a high approximation accuracy of the interpolation kernel for each particle, the smoothing length is adjusted adaptively according to a defined scaling  ... 
doi:10.1016/j.vrih.2018.12.001 doaj:c323962a2c5e4dc9b49a6e9dff84e575 fatcat:3w2mdquxxnhh7mwhyowq65bsqa
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