Bandwidth Prediction based on Nu-Support Vector Regression and Parallel Hybrid Particle Swarm Optimization [PDF]
International Journal of Computational Intelligence Systems, 2010 This paper addresses the problem of generating multi-step-ahead bandwidth prediction. Variation of bandwidth is modeled as a Nu-Support Vector Regression (Nu-SVR) procedure. A parallel procedure is proposed to hybridize constant and binary Particle Swarm
Liang Hu, Xilong Che, Xiaochun Cheng
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Modal clustering asymptotics with applications to bandwidth selection [PDF]
Electronic Journal of Statistics, 2019 Density-based clustering relies on the idea of linking groups to some specific features of the probability distribution underlying the data. The reference to a true, yet unknown, population structure allows to frame the clustering problem in a standard ...
A. Casa, Jos'e E. Chac'on, G. Menardi
semanticscholar +1 more source
The Priestley-Chao Estimator of Conditional Density with Uniformly Distributed Random Design [PDF]
Statistika: Statistics and Economy Journal, 2018 The present paper is focused on non-parametric estimation of conditional density. Conditional density can be regarded as a generalization of regression thus the kernel estimator of conditional density can be derived from the kernel estimator of the ...
Kateřina Konečná
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On The Feasibility Of Centrally-Coordinated Peer-To-Peer Live Streaming [PDF]
, 2011 In this paper we present an exploration of central coordination as a way of managing P2P live streaming overlays. The main point is to show the elements needed to construct a system with that approach.
Amgad Naiem +6 more
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Best Possible Constant for Bandwidth Selection
The Annals of Statistics, 1992 For the data based choice of the bandwidth of a kernel density estimator, several methods have recently been proposed which have a very fast asymptotic rate of convergence to the optimal bandwidth. In particular the relative rate of convergence is the square root of the sample size, which is known to be the best possible.
Fan, Jianqing, Marron, James S.
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The Mean and Median Criteria for Kernel Bandwidth Selection for Support Vector Data Description [PDF]
2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017 Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of ...
A. Chaudhuri +4 more
semanticscholar +1 more source
Bandwidth selection for kernel density estimators of multivariate level sets and highest density regions [PDF]
, 2018 We consider bandwidth matrix selection for kernel density estimators (KDEs) of density level sets in $\mathbb{R}^d$, $d \ge 2$. We also consider estimation of highest density regions, which differs from estimating level sets in that one specifies the ...
Charles R. Doss, Guangwei Weng
semanticscholar +1 more source
Joint User Grouping, Version Selection, and Bandwidth Allocation for Live Video Multicasting
IEEE Transactions on Communications, 2022 The key challenges in live video multicasting include how to properly form multicast groups, select video versions and allocate wireless resources, in order to guarantee the quality of experience (QoE) while ensuring low latency delivery.
Zhilong Zhang +6 more
semanticscholar +1 more source
Asymptotics and optimal bandwidth selection for highest density region estimation [PDF]
, 2009 We study kernel estimation of highest-density regions (HDR). Our main contributions are two-fold. First, we derive a uniform-in-bandwidth asymptotic approximation to a risk that is appropriate for HDR estimation. This approximation is then used to derive
Samworth, R. J., Wand, M. P.
core +5 more sources
Bandwidth Selection Problem in Nonparametric Functional Regression [PDF]
Statistika: Statistics and Economy Journal, 2017 The focus of this paper is the nonparametric regression where the predictor is a functional random variable, and the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this purpose. The two key problems
Daniela Kuruczová, Jan Koláček
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