A new parametric method of estimating the joint probability density: revisited

DOI10.1016/j.physa.2019.121455OpenAlexW2944407335MaRDI QIDQ2161806

Moawia Alghalith

Publication date: 5 August 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2019.121455

zbMATH Keywords

Taylor expansionconditional densityparametric density estimationmarginal densityjoint probability density

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Cites Work

• A kernel-based parametric method for conditional density estimation
• A new parametric method of estimating the joint probability density
• Bayesian inference for conditional copulas using Gaussian process single index models
• Recent progress in log-concave density estimation
• Geometry of discrete copulas
• On structure, family and parameter estimation of hierarchical Archimedean copulas
• On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation
• Elements of Copula Modeling with R
• Generalized information matrix tests for copulas
• Unnamed Item
• Unnamed Item

This page was built for publication: A new parametric method of estimating the joint probability density: revisited