Results 21 to 30 of about 411,514 (294)
Proceedings, 2018 Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed ...
Inés Barbeito, Ricardo Cao
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A New Kernel Estimator of Copulas Based on Beta Quantile Transformations
Mathematics, 2021 A copula is a multivariate cumulative distribution function with marginal distributions Uniform(0,1). For this reason, a classical kernel estimator does not work and this estimator needs to be corrected at boundaries, which increases the difficulty of ...
Catalina Bolancé, Carlos Alberto Acuña
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Adaptive Warped Kernel Estimators [PDF]
Scandinavian Journal of Statistics, 2014 AbstractIn this work, we develop a method of adaptive non‐parametric estimation, based on ‘warped’ kernels. The aim is to estimate a real‐valued function s from a sample of random couples (X,Y). We deal with transformed data (Φ(X),Y), with Φ a one‐to‐one function, to build a collection of kernel estimators.
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Bivariate box plots based on quantile regression curves
Dependence Modeling, 2020 In this paper, we propose a procedure to build bivariate box plots (BBP). We first obtain the theoretical BBP for a random vector (X, Y). They are based on the univariate box plot of X and the conditional quantile curves of Y|X. They can be computed from
Navarro Jorge
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Adaptive Kernel Density Estimation [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2003 This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an “adaptive kernel” approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated ...
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Entropy, 2008 At the heart of many ICA techniques is a nonparametric estimate of an information measure, usually via nonparametric density estimation, for example, kernel density estimation.
Julian Sorensen
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A Deconvolutional Deblurring Algorithm Based on Short- and Long-Exposure Images
Sensors, 2022 An iterative image restoration algorithm, directed at the image deblurring problem and based on the concept of long- and short-exposure deblurring, was proposed under the image deconvolution framework by investigating the imaging principle and existing ...
Yang Bai, Zheng Tan, Qunbo Lv, Min Huang
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The Augmented Complex Kernel LMS
, 2011 Recently, a unified framework for adaptive kernel based signal processing of complex data was presented by the authors, which, besides offering techniques to map the input data to complex Reproducing Kernel Hilbert Spaces, developed a suitable Wirtinger ...
Bouboulis, Pantelis +2 more
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Variable Kernel Density Estimation
The Annals of Statistics, 1992 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Terrell, George R., Scott, David W.
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Journal of Statistical Software, 2018 evmix is an R package (R Core Team 2017) with two interlinked toolsets: i) for extreme value modeling and ii) kernel density estimation. A key issue in univariate extreme value modeling is the choice of threshold beyond which the asymptotically motivated
Yang Hu, Carl Scarrott
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