Results 71 to 80 of about 3,774,080 (285)
Generalised Exponential Kernels for Nonparametric Density Estimation
This paper introduces a novel kernel density estimator (KDE) based on the generalised exponential (GE) distribution, designed specifically for positive continuous data. The proposed GE KDE offers a mathematically tractable form that avoids the use of special functions, for instance, distinguishing it from the widely used gamma KDE, which relies on the ...
Craig, Laura M., Barreto-Souza, Wagner
openaire +2 more sources
Business Strategy and the Environment, EarlyView.ABSTRACT The circular economy (CE) has become a major focus in energy and environmental (E&E) research, as it helps advance sustainable development, improve resource efficiency and alleviate environmental pressure. Even so, the literature is still fragmented, particularly in the CE themes it explores, the levels of analysis it adopts and the methods ...
Jahira Debbarma +2 more
wiley +1 more source
Nonparametric Kernel Smoothing Methods. The sm library in Xlisp-Stat
Journal of Statistical Software, 2001 In this paper we describe the Xlisp-Stat version of the sm library, a software for applying nonparametric kernel smoothing methods. The original version of the sm library was written by Bowman and Azzalini in S-Plus, and it is documented in their book ...
Luca Scrucca
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Business Strategy and the Environment, EarlyView.ABSTRACT The growing frequency of global crises has intensified concerns regarding climate vulnerability and the resilience of global production systems. This study examines the heterogeneous effects of supply chain development and supply chain digitalisation on climate vulnerability across countries, while accounting for institutional and investment ...
Ziwei Li +4 more
wiley +1 more source
BatteriesThis work develops a hybrid kernel sparse Gaussian process regression integrated with kernel density estimation (HCSGPR-UQ) to resolve three critical drawbacks of conventional lithium-ion battery State of Energy (SOE) estimators: degraded accuracy under ...
Chaoyu Xiao +5 more
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Gamma Kernel Estimators for Density and Hazard Rate of Right-Censored Data
Journal of Probability and Statistics, 2011 The nonparametric estimation for the density and hazard rate functions for right-censored data using the kernel smoothing techniques is considered. The “classical” fixed symmetric kernel type estimator of these functions performs well in the interior ...
T. Bouezmarni, A. El Ghouch, M. Mesfioui
doaj +1 more source
Estimation of Star-Shaped Distributions
Risks, 2016 Scatter plots of multivariate data sets motivate modeling of star-shaped distributions beyond elliptically contoured ones. We study properties of estimators for the density generator function, the star-generalized radius distribution and the density in a
Eckhard Liebscher, Wolf-Dieter Richter
doaj +1 more source
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R [PDF]
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing.
Tarn Duong
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The Canadian Journal of Chemical Engineering, EarlyView.Abstract This study investigates the effect of moisture on CO2 adsorption in South African coals using both experimental and machine learning approaches. Three coal samples (SL, TN, and EM) with varying ranks (RoVmr: 3.49%, 1.26%, and 0.64%, respectively) were collected from different regions of South Africa.
Kasturie Premlall +3 more
wiley +1 more source
A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation. [PDF]
Multivariate kernel regression is an important tool for investigating the relationship between a response and a set of explanatory variables. It is generally accepted that the performance of a kernel regression estimator largely depends on the choice of ...
Robert D. Brooks +2 more
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