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Robust kernels for kernel density estimation
Economics Letters, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Shaoping +3 more
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Choice of Kernel Function for Density Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980Let l=f^n(x) be the kernel estimate of a density f(x) from a sample of size n. Wahba [6] has developed an upper bound to E[f(x)-l=f^n(x)]2. In the present paper, we find the kernel function of finite support [m=-T, T] that minimizes Wahba's upper bound. It is Q(y) = (1 + am=-1) (2T)m=-1 [1-m=-a|y|a] where a = 2-pm=-1, p m=ge 1.
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Bootstrapping kernel spectral density estimates with kernel bandwidth estimation
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004We address the problem of confidence interval estimation of spectral densities using the bootstrap. Of special interest is the choice of the kernel global bandwidth. First, we investigate resampling based techniques for the choice of the bandwidth.
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Kernel density estimation in adaptive tracking
2008 47th IEEE Conference on Decision and Control, 2008We investigate the asymptotic properties of a recursive kernel density estimator associated with the driven noise of multivariate ARMAX models in adaptive tracking. We establish an almost sure pointwise and uniform strong law of large numbers as well as a pointwise and multivariate central limit theorem.
Bernard Bercu, Bruno Portier
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Online Discriminative Kernel Density Estimation
2010 20th International Conference on Pattern Recognition, 2010We propose a new method for online estimation of probabilistic discriminative models. The method is based on the recently proposed online Kernel Density Estimation (oKDE) framework which produces Gaussian mixture models and allows adaptation using only a single data point at a time.
Matej Kristan, Ales Leonardis
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On Consistency of a Kernel Estimate of a Density Function
Calcutta Statistical Association Bulletin, 1984Given a sequence of i. i. d. random variables the Lr( r⩾ 1) convergence of kernel type estimators of their common density function is studied. Also, strong consistency of the estimators is considered in the case when the random variables form a stationary sequence not necessarily i. i. d.
Basu, A. K., Sahoo, D. K.
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2001
In this chapter, we get our first taste of real analysis, starting with some results on the approximations of functions in L 1. The literature on this is vast, and a lot of it is ancient. The problem is that f cannot be approximated in L 1 by μ n , the empirical measure, as the total variation distance between any density f and any atomic measure (like
Luc Devroye, Gábor Lugosi
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In this chapter, we get our first taste of real analysis, starting with some results on the approximations of functions in L 1. The literature on this is vast, and a lot of it is ancient. The problem is that f cannot be approximated in L 1 by μ n , the empirical measure, as the total variation distance between any density f and any atomic measure (like
Luc Devroye, Gábor Lugosi
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Kernel Density Estimation on a Linear Network
Scandinavian Journal of Statistics, 2016AbstractThis paper develops a statistically principled approach to kernel density estimation on a network of lines, such as a road network. Existing heuristic techniques are reviewed, and their weaknesses are identified. The correct analogue of the Gaussian kernel is the ‘heat kernel’, the occupation density of Brownian motion on the network.
Mcswiggan, G. +2 more
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Isolation Kernel Density Estimation
2021 IEEE International Conference on Data Mining (ICDM), 2021Kai Ming Ting +3 more
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Kernel Density Estimation for Compositional Data
Applied Statistics, 1985Summary: Although rich parametric families of distributions over the simplex now exist for describing patterns of variability of compositional data, there remain problems where such descriptions fail. For such cases this paper suggests two main kernel methods of density estimation and compares their performance on real and simulated data sets.
Aitchison, J., Lauder, I. J.
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