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Bayesian selector of adaptive bandwidth for multivariate gamma kernel estimator on [0,∞ )d [PDF]

open access: yesJournal of Applied Statistics, 2021
Bayesian bandwidth selections in multivariate associated kernel estimation of probability density functions are known to improve classical methods such as cross-validation techniques in terms of execution time and smoothing quality. The paper focuses on a basic multivariate gamma kernel which is appropriated to estimate densities with support [ 0 , ∞ )
Sobom M. Somé, Célestin C. Kokonendji
openaire   +3 more sources

The adaptive gamma-BSPE kernel density estimation for nonnegative heavy-tailed data

open access: yesJournal of Innovative Applied Mathematics and Computational Sciences, 2022
In this work, we consider the nonparametric estimation of the probability density function for nonnegative heavy-tailed (HT) data. The objective is first to propose a new estimator that will combine two regions of observations (high and low density ...
Yasmina ZIANE   +2 more
doaj   +4 more sources

Bandwidth Selectors on Semiparametric Bayesian Networks

open access: yesInformation Sciences
Semiparametric Bayesian networks (SPBNs) integrate parametric and non-parametric probabilistic models, offering flexibility in learning complex data distributions from samples. In particular, kernel density estimators (KDEs) are employed for the non-parametric component.
Víctor Alejandre   +2 more
openaire   +2 more sources

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