Everything about Multivariate kernel density estimation
Concept in statistics mathematics
Kernel density estimation is a nonparametric technique for density estimation i.e., estimation of probability density functions, which is one of the fundamental questions in statistics. It can be viewed as a generalisation of histogram density estimation with improved statistical properties. Apart from histograms, other types of density estimators include parametric, spline, wavelet and Fourier series. Kernel density estimators were first introduced in the scientific literature for univariate data in the 1950s and 1960s and subsequently have been widely adopted. It was soon recognised that analogous estimators for multivariate data would be an important addition to multivariate statistics. Based on research carried out in the 1990s and 2000s, multivariate kernel density estimation has reached a level of maturity comparable to its univariate counterparts. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Multivariate kernel density estimationA 5-minute orientation
- Skim a recent paper: Gaussian Mixture Model for Multivariate Wind Power Based on Kernel Density Estimation and Component Number ReductionInfluential work since 2022
- Pick up a classic book: The Elements of Statistical LearningThe most-reprinted book on the subject
- Read the landmark paper: ks : Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in RCited 524 times
- Go sideways: Kernel density estimationA closely related topic
- Go sideways: Nonparametric statisticsA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
ks : Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation
Multivariate online kernel density estimation with Gaussian kernels
A Bayesian approach to bandwidth selection for multivariate kernel density estimation
Feature significance for multivariate kernel density estimation
Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation
Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimation
Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating
🆕 Influential since 2022
Gaussian Mixture Model for Multivariate Wind Power Based on Kernel Density Estimation and Component Number Reduction
Multivariate probabilistic prediction of dam displacement behaviour using extended Seq2Seq learning and adaptive kernel density estimation
Multivariate global agricultural drought frequency analysis using kernel density estimation
Confidence-based design optimization using multivariate kernel density estimation under insufficient input data
Supervised Multivariate Kernel Density Estimation for Enhanced Plasma Etching Endpoint Detection
📚 Books worth reading
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
The Jackknife and BootstrapJun Shao, Dongsheng Tu · 1995
Density Estimation for Statistics and Data AnalysisB. W. Silverman · 1986
Nonparametric econometric methodsQi Li, Jeffrey Scott Racine · 2009
Data representations, transformations, and statistics for visual reasoningRoss Maciejewski · 2011
Statistics and manufacturing with subthemes in environmental statistics, graphics and imagingSymposium on the Interface: Computing Science and Statistics (27th 1995 Pittsburgh, Pa.) · 1996
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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