Approximation Multivariate Distribution with Pair Copula Using the Orthonormal Polynomial and Legendre Multiwavelets Basis Functions
DOI10.1080/03610918.2013.804557zbMath1337.62116arXiv1210.7485OpenAlexW1775360069MaRDI QIDQ2809615
No author found.
Publication date: 30 May 2016
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1210.7485
vinepair-copula constructionLegendre multiwaveletsdensity approximationminimum information techniqueorthonormal polynomial series
Density estimation (62G07) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Nonparametric estimation (62G05) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Approximations to statistical distributions (nonasymptotic) (62E17)
Related Items
Cites Work
- Pair-copula constructions of multiple dependence
- Solution of Hallen's integral equation using multiwavelets
- Entropy minimization, \(DAD\) problems, and doubly stochastic kernels
- Generalized autoregressive conditional heteroscedasticity
- Vines -- a new graphical model for dependent random variables.
- Adaptive solution of partial differential equations in multiwavelet bases
- Probability density decomposition for conditionally dependent random variables modeled by vines
- Uncertainty Analysis with High Dimensional Dependence Modelling
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- On a measure of lack of fit in time series models
- Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
- Minimally informative distributions with given rank correlation for use in uncertainty analysis
This page was built for publication: Approximation Multivariate Distribution with Pair Copula Using the Orthonormal Polynomial and Legendre Multiwavelets Basis Functions
