Body tail adaptive kernel density estimation for nonnegative heavy-tailed data

DOI10.1515/mcma-2021-2082zbMath1467.62059OpenAlexW3126566487MaRDI QIDQ2031303

Nabil Zougab, Smail Adjabi, Yasmina Ziane

Publication date: 9 June 2021

Published in: Monte Carlo Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/mcma-2021-2082

zbMATH Keywords

MCMC methodkernel density estimationprior distributioncross validationheavy-tailed dataBayesian bandwidth selectorBS-PE kernel

Mathematics Subject Classification ID

Density estimation (62G07) Statistics of extreme values; tail inference (62G32) Monte Carlo methods (65C05) Large deviations (60F10)

Related Items

Asymptotic properties of Dirichlet kernel density estimators

Uses Software

• Gibbsit

Cites Work

• Unnamed Item
• Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions
• Density estimation using asymmetric kernels and Bayes bandwidths with censored data
• On the hazard function of Birnbaum-Saunders distribution and associated inference
• Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation
• Birnbaum-Saunders power-exponential kernel density estimation and Bayes local bandwidth selection for nonnegative heavy tailed data
• Inference from iterative simulation using multiple sequences
• Generalized Birnbaum-Saunders kernel density estimators and an analysis of financial data
• A Bayesian method to estimate the optimal bandwidth for multivariate kernel estimator
• Binomial kernel and Bayes local bandwidth in discrete function estimation
• Bayesian approach to the choice of smoothing parameter in kernel density estimation
• Density estimation using inverse and reciprocal inverse Gaussian kernels
• Adaptive Bayesian bandwidth selection in asymmetric kernel density estimation for nonnegative heavy-tailed data
• Equation of State Calculations by Fast Computing Machines
• Adaptive smoothing in associated kernel discrete functions estimation using Bayesian approach
• Bayes bandwidth selection in kernel density estimation with censored data
• Monte Carlo sampling methods using Markov chains and their applications
• Probability density function estimation using gamma kernels