Results 71 to 80 of about 257,461 (188)
Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data [PDF]
Journal of Sciences, Islamic Republic of Iran, 2014 Kernel density estimators are the basic tools for density estimation in non-parametric statistics. The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in which the bandwidth is varied depending on the ...
V. Fakoor
doaj
On the Way to Recovery: A Nonparametric Bias Free Estimation of Recovery Rate Densities [PDF]
In this paper we analyse recovery rates on defaulted bonds using the Standard and Poor’s/PMD database for the years 1981-1999. Due to the specific nature of the data (observations lie within 0 and 1), we must rely on nonstandard econometric techniques ...
Olivier RENAULT, Olivier SCAILLET
core
Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC [PDF]
We provide Markov chain Monte Carlo (MCMC) algorithms for computing the bandwidth matrix for multivariate kernel density estimation. Our approach is based on treating the elements of the bandwidth matrix as parameters to be estimated, which we do by ...
Maxwell L. King +2 more
core +3 more sources
Weakly supervised segment annotation via expectation kernel density estimation
IET Computer Vision, 2019 Since the labelling for the positive images/videos is ambiguous in weakly supervised segment annotation, negative mining‐based methods that only use the intra‐class information emerge. In these methods, negative instances are utilised to penalise unknown
Liantao Wang, Qingwu Li, Jianfeng Lu
doaj +1 more source
Kernel density estimation on the Siegel space applied to radar processing
, 2016 Main techniques of probability density estimation on Riemannian manifolds are reviewed in the case of the Siegel space. For computational reasons we chose to focus on the kernel density estimation.
Angulo, Jesus +2 more
core +1 more source
ROBUST KERNEL ESTIMATOR FOR DENSITIES OF UNKNOWN [PDF]
Results on nonparametric kernel estimators of density differ according to the assumed degree of density smoothness; it is often assumed that the density function is at least twice differentiable.
Victoria Zinde-Walsh, Yulia Kotlyarova
core
Ultrasound Entropy Imaging Based on the Kernel Density Estimation: A New Approach to Hepatic Steatosis Characterization. [PDF]
Diagnostics (Basel), 2023 Gao R +5 more
europepmc +1 more source
, 2018 In this article we perform an asymptotic analysis of Bayesian parallel density estimators which are based on logspline density estimation. The parallel estimator we introduce is in the spirit of a kernel density estimator introduced in recent studies. We
Conlon, Erin +2 more
core
Nonparametric Density Estimation for Positive Time Series [PDF]
The Gaussian kernel density estimator is known to have substantial problems for bounded random variables with high density at the boundaries. For i.i.d. data several solutions have been put forward to solve this boundary problem. In this paper we propose
Jeroen V.K. Rombouts, Taoufik Bouezmarni
core
Adaptive Clustering Using Kernel Density Estimators
, 2017 We derive and analyze a generic, recursive algorithm for estimating all splits in a finite cluster tree as well as the corresponding clusters. We further investigate statistical properties of this generic clustering algorithm when it receives level set estimates from a kernel density estimator.
Steinwart, Ingo +2 more
openaire +3 more sources