Results 31 to 40 of about 2,848,958 (338)
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
doaj +1 more source
A Doubly Smoothed PD Estimator in Credit Risk
Proceedings, 2020 In this work a doubly smoothed probability of default (PD) estimator is proposed based on a smoothed version of the survival Beran’s estimator. The asymptotic properties of both the smoothed survival and PD estimators are proved and their behaviour is ...
Rebeca Peláez Suárez +2 more
doaj +1 more source
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 ...
openaire +3 more sources
Nebulosa recovers single cell gene expression signals by kernel density estimation
bioRxiv, 2020 Summary Data sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualised in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost
José Alquicira-Hernández, J. Powell
semanticscholar +1 more source
Nonparametric Estimation of Extreme Quantiles with an Application to Longevity Risk
Risks, 2021 A new method to estimate longevity risk based on the kernel estimation of the extreme quantiles of truncated age-at-death distributions is proposed. Its theoretical properties are presented and a simulation study is reported.
Catalina Bolancé, Montserrat Guillen
doaj +1 more source
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
doaj +1 more source
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
core +1 more source
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.
openaire +2 more sources
kdecopula: An R Package for the Kernel Estimation of Bivariate Copula Densities [PDF]
, 2016 We describe the R package kdecopula (current version 0.9.0), which provides fast implementations of various kernel estimators for the copula density. Due to a variety of available plotting options it is particularly useful for the exploratory analysis of
T. Nagler
semanticscholar +1 more source
Optimal kernel estimation of spot volatility of stochastic differential equations [PDF]
Stochastic Processes and their Applications, 2016 Kernel Estimation is one of the most widely used estimation methods in non-parametric Statistics, having a wide-range of applications, including spot volatility estimation of stochastic processes.
Jos'e E. Figueroa-L'opez, Cheng Li
semanticscholar +1 more source