Results 1 to 10 of about 52,951 (307)
Multivariate kernel density estimation with a parametric support [PDF]
Opuscula Mathematica, 2009 We consider kernel density estimation in the multivariate case, focusing on the use of some elements of parametric estimation. We present a two-step method, based on a modification of the EM algorithm and the generalized kernel density estimator, and ...
Jolanta Jarnicka
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Nonparametric Multivariate Density Estimation: Case Study of Cauchy Mixture Model
Mathematics, 2021 Estimation of probability density functions (pdf) is considered an essential part of statistical modelling. Heteroskedasticity and outliers are the problems that make data analysis harder. The Cauchy mixture model helps us to cover both of them.
Tomas Ruzgas +2 more
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The density of multivariate $M$-estimates [PDF]
The Annals of Statistics, 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Almudevar, Anthony +2 more
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Supervised Multivariate Kernel Density Estimation for Enhanced Plasma Etching Endpoint Detection
IEEE Access, 2022 The advancement of semiconductor technology nodes requires precise control of their manufacturing process, including plasma etching, which is highly important in terms of the yield, cost, and device performance.
Jungyu Choi +3 more
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Advances in Mathematical Physics, 2023 There are some previous works on designing efficient and high-order numerical methods of density estimation for stochastic partial differential equation (SPDE) driven by multivariate Gaussian random variables.
Hongling Xie
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Mathematics, 2022 Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE).
Jenny Farmer +2 more
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Projection-based estimation of multivariate distribution density
Lietuvos Matematikos Rinkinys, 2002 There is not abstract.
Mindaugas Kavaliauskas, Rimantas Rudzkis
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Efficient Density Estimation for High-Dimensional Data
IEEE Access, 2022 Multivariate density estimation methods typically work well in low dimensions and their extension to data analytics in high dimensions domain has proven challenging. For density estimation in high-dimensional big data domains, the non-parametric Bayesian
Aref Majdara, Saeid Nooshabadi
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Wavelet Density and Regression Estimators for Functional Stationary and Ergodic Data: Discrete Time
Mathematics, 2022 The nonparametric estimation of density and regression function based on functional stationary processes using wavelet bases for Hilbert spaces of functions is investigated in this paper. The mean integrated square error over adapted decomposition spaces
Sultana DIDI +2 more
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Mathematics, 2022 In this study, we look at the wavelet basis for the nonparametric estimation of density and regression functions for continuous functional stationary processes in Hilbert space. The mean integrated squared error for a small subset is established.
Sultana Didi, Salim Bouzebda
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