Copula density estimation by total variation penalized likelihood with linear equality constraints

A copula density is the joint probability density function (PDF) of a random vector with uniform marginals. An approach to bivariate copula density estimation is introduced that is based on maximum penalized likelihood estimation (MPLE) with a total variation (TV) penalty term. The marginal unity an...

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Bibliographic Details
Published in:	Computational statistics & data analysis Vol. 56; no. 2; pp. 384 - 398
Main Authors:	Qu, Leming, Yin, Wotao
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.02.2012 Elsevier
Series:	Computational Statistics & Data Analysis
Subjects:	Algorithms Augmented Lagrangian method Computational efficiency Computer simulation Copula density estimation Copula density estimation Total variation Maximum penalized likelihood estimation Augmented Lagrangian method Density Exact sciences and technology General topics Mathematics Matlab Maximum penalized likelihood estimation Multivariate analysis Nonparametric inference Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Probability and statistics Probability density functions Sciences and techniques of general use Statistics Television Total variation Total variation Augmented Lagrangian method Copula density estimation Maximum penalized likelihood estimation Rank statistic Density estimation Optimization method Non parametric estimation Cross validation Random vector Multivariate analysis Penalty method Parametric method Linear constraint Marginal distribution Operator splitting Probability density function Ill posed problem Lagrangian method Symmetry Copulas Discriminant analysis Data analysis Lagrangian Numerical linear algebra Statistical estimation Algorithm Constrained optimization Regularization method Selection problem Numerical analysis Statistical computation Simulation Joint probability Equality constraint Maximum likelihood Regularization
ISSN:	0167-9473, 1872-7352
Online Access:	Get full text
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