Results 251 to 260 of about 3,774,080 (285)
Some of the next articles are maybe not open access.
Efficient On-Line Nonparametric Kernel Density Estimation
Algorithmica, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christophe G. Lambert +3 more
openaire +2 more sources
Nonparametric kernel estimation for error density
Proceedings of 1994 Workshop on Information Theory and Statistics, 2002Summary form only given. Consider a linear model, y/sub i/=x'/sub i//spl beta/+e/sub i/, i=1,2,..., x'/sub i/s are p(/spl ges/1) dimension known vectors and /spl beta/(/spl isin/R/spl deg/) is an unknown parametric vector and e/sub i/ are assumed i.i.d.r.v.'s from a common unknown density function f(x) with med (e/sub i/)=0.
null Zhu Yu Li, null Shu Zhao Zou
openaire +1 more source
Nonparametric saliency detection using kernel density estimation
2010 IEEE International Conference on Image Processing, 2010This paper proposes a nonparametric saliency model based on kernel density estimation (KDE) mainly aiming at content-based applications such as salient object segmentation. A set of KDE models are constructed on the basis of regions segmented using the mean shift algorithm.
Zhi Liu 0003 +3 more
openaire +2 more sources
Empirical Bayes nonparametric kernel density estimation
Statistics & Probability Letters, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ker, Alan P., Ergün, A. T.
openaire +2 more sources
Kernel-Based Hybrid Random Fields for Nonparametric Density Estimation
2010Hybrid random fields are a recently proposed graphical model for pseudo-likelihood estimation in discrete domains. In this paper, we develop a continuous version of the model for nonparametric density estimation. To this aim, Nadaraya-Watson kernel estimators are used to model the local conditional densities within hybrid random fields.
Freno, Antonino +2 more
openaire +2 more sources
Nonparametric kernel estimation of conditional copula density
Statistics & Probability LetterszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Toihir Soulaimana Djaloud +1 more
openaire +1 more source
Nonparametric kernel density estimation for general grouped data
Journal of Nonparametric Statistics, 2016Interval-grouped data are defined, in general, when the event of interest cannot be directly observed and it is only known to have been occurred within an interval. In this framework, a nonparametric kernel density estimator is proposed and studied.
Miguel Reyes +2 more
openaire +1 more source
Nonparametric density estimation based on beta prime kernel
Communications in Statistics - Theory and Methods, 2018AbstractIn this work, we propose beta prime kernel estimator for estimation of a probability density functions defined with nonnegative support.
Elif Erçelik, Mustafa Nadar
openaire +1 more source
Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019In this paper, we propose a statistical method to predict the onset of bradycardia in preterm infants without any prior knowledge. To model information on the QRS complex R wave, we exploit nonparametric methods to estimate the density. Our proposed method takes advantage of the kernel density estimator in order to provide a statistical guarantee of 95%
Subhasish Das +4 more
openaire +2 more sources
Nonparametric estimation of reliability function using the kernel density estimation method
1994 Proceedings. 44th Electronic Components and Technology Conference, 2002Analysis of data from an accelerated life test employs a model. Such a statistical model for an accelerated life test consists of a life distribution that represents the scatter in product life and a relationship between life and stress. In this study, the Coffin-Manson relationship is used to model fatigue failure of metals subject to thermal cycling.
null Oh-Gone Chun +6 more
openaire +1 more source