variable kernel estimatenonparametric estimationpartitionshatter coefficientIt is well-established that one can improve performance of kernel density estimates by varying the bandwidth with the location and/or the sample data at hand. Our interest in this paper is in the data-based selection of a ...
Similar modifications should be made to the estimators treated by other authors in the context of variable kernel density estimation, if the good convergence rate properties claimed for these estimators are to be ensured. Details of the necessary alterations, including several practical proposals, are ...
Variable kernel methods in density estimation , 1993 Google Scholar [16] Müller H., Stadtmüller U. Variable bandwidth kernel estimators of regression curves. Ann. Statist , 1987 , 15: 182 -201 Google Scholar [17] Nadaraya. E.A. On estimating regression. Theory Prob. Appl , 1964...
The kernel density estimator with adaptive varying bandwidth (KDE-AVB) estimates the probability density for the given data in a point-wise manner, which determines the optimal bandwidth based on the intersection of confidence intervals (ICI) rule. In or
The modal distributions of olivine abundance in the compiled cratonic xenolith populations were statistically assessed with Kernel Density Estimation (KDE). To facilitate comparisons with experimental and modelled residues produced at temperatures above the solidus, we also assessed the modal distribution of...
Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation 2022, Information Retrieval JournalDebasis Ganguly is a research scientist at IBM Research, Ireland. His research interests include applications of semantic relationships between words, specifically wit...
region. Kernel density estimates of relative brain size for the observed sample ofnspecies that occur within each environmental region (solid black lines in right column) are compared to 10,000 null density estimates, each derived fromnrandomly sampled species (1000 examples plotted as light gray ...
Also see [R] npregress series — Nonparametric series regression [R] npregress series postestimation — Postestimation tools for npregress series [R] npregress intro — Introduction to nonparametric regression [R] kdensity — Univariate kernel density estimation [R] lpoly — Kernel-weighted local ...
Mixture Distribution: Mixture distribution is one of the significant latent variable models used for guesstimating the underlying density function. Notably, it delivers an overall outline for density estimation with the simpler parametric distributions. The expectation-maximization (EM) procedure is one of...
(2016a) presented a novel way to dynamically forecast sampling exercise patterns, poor sampling matrix index, and electricity rates using the latest density estimation from the approximate nearest neighbors (DEANN) algorithm. The suggested model is a hybrid scheme of supervised and unsupervised ...