Multivariate kernel density estimation

<p><a href="/facts/Kernel_density_estimation/NmznyEW8">Kernel density estimation</a> is a <a href="/facts/Nonparametric/svTDJHTb">nonparametric</a> technique for  <a href="/facts/Density_estimation/YthqJssX">density estimation</a> i.e., estimation of <a href="/facts/Probability_density_function/zvfybna4">probability density functions</a>, which is one of the fundamental questions in <a href="/facts/Statistics/DNPsKGYU">statistics</a>. It can be viewed as a generalisation of <a href="/facts/Histogram/oMmvqGEA">histogram</a> density estimation with improved statistical properties. Apart from histograms, other types of density estimators include <a href="/facts/Parametric_statistics/Uvq9SJV9">parametric</a>, <a href="/facts/Spline_interpolation/AowdpQwm">spline</a>, <a href="/facts/Wavelet/3XcX4uIP">wavelet</a> and <a href="/facts/Fourier_series/s3fsxPAT">Fourier series</a>. Kernel density estimators were first introduced in the scientific literature for <a href="/facts/Univariate/PYwC7u5H">univariate</a> 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 <a href="/facts/Multivariate_statistics/tDuNcWrL">multivariate statistics</a>. 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.
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Multivariate kernel density estimation open-in-new

Multivariate kernel density estimation