Semiparametric multivariate density estimation for positive data using copulas
T. Bouezmarni and
Jeroen Rombouts
Computational Statistics & Data Analysis, 2009, vol. 53, issue 6, 2040-2054
Abstract:
The estimation of density functions for positive multivariate data is discussed. The proposed approach is semiparametric. The estimator combines gamma kernels or local linear kernels, also called boundary kernels, for the estimation of the marginal densities with parametric copulas to model the dependence. This semiparametric approach is robust both to the well-known boundary bias problem and the curse of dimensionality problem. Mean integrated squared error properties, including the rate of convergence, the uniform strong consistency and the asymptotic normality are derived. A simulation study investigates the finite sample performance of the estimator. The proposed estimator performs very well, also for data without boundary bias problems. For bandwidths choice in practice, the univariate least squares cross validation method for the bandwidth of the marginal density estimators is investigated. Applications in the field of finance are provided.
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00301-0
Full text for ScienceDirect subscribers only.
Related works:
Working Paper: Semiparametric multivariate density estimation for positive data using copulas (2007) 
Working Paper: Semiparametric Multivariate Density Estimation for Positive Data Using Copulas (2007) 
Working Paper: Semiparametric Multivariate Density Estimation for Positive Data Using Copulas (2007) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:6:p:2040-2054
Access Statistics for this article
Computational Statistics & Data Analysis is currently edited by S.P. Azen
More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().