kdecopula: An R Package for the Kernel Estimation of Bivariate Copula Densities

We describe the R package kdecopula (current version 0.9.0), which provides fast implementations of various kernel estimators for the copula density. Due to a variety of available plotting options it is particularly useful for the exploratory analysis of dependence structures. It can be further used for accurate nonparametric estimation of copula densities and resampling. The implementation features spline interpolation of the estimates to allow for fast evaluation of density estimates and integrals thereof. We utilize this for a fast renormalization scheme that ensures that estimates are bona fide copula densities and additionally improves the estimators' accuracy. The performance of the methods is illustrated by simulations.

[1]  Thomas Nagler Kernel Methods for Vine Copula Estimation , 2014 .

[2]  R. Nelsen An Introduction to Copulas (Springer Series in Statistics) , 2006 .

[3]  Arthur Charpentier,et al.  Probit Transformation for Nonparametric Kernel Estimation of the Copula Density , 2014, 1404.4414.

[4]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[5]  A. Frigessi,et al.  Pair-copula constructions of multiple dependence , 2009 .

[6]  I. Gijbels,et al.  Positive quadrant dependence tests for copulas , 2010 .

[7]  L. Devroye,et al.  Nonparametric Density Estimation: The L 1 View. , 1985 .

[8]  Gal Elidan,et al.  Copulas in Machine Learning , 2013 .

[9]  Jeffrey S. Racine,et al.  Nonparametric Econometrics: The np Package , 2008 .

[10]  C. De Michele,et al.  On the Use of Copulas in Hydrology: Theory and Practice , 2007 .

[11]  E. Luciano,et al.  Copula methods in finance , 2004 .

[12]  Colin Aitken,et al.  Evaluation of trace evidence in the form of multivariate data , 2004 .

[13]  J. Mielniczuk,et al.  Estimating the density of a copula function , 1990 .

[14]  Jeffrey S. Racine,et al.  Mixed data kernel copulas , 2015 .

[15]  Olivier Scaillet,et al.  The estimation of copulas : theory and practice , 2007 .

[16]  C. Genest,et al.  A Primer on Copulas for Count Data , 2007, ASTIN Bulletin.

[17]  H. Joe Relative Entropy Measures of Multivariate Dependence , 1989 .

[18]  Francesca Cagnacci,et al.  The home-range concept: are traditional estimators still relevant with modern telemetry technology? , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[19]  Thomas Nagler,et al.  A generic approach to nonparametric function estimation with mixed data , 2017, Statistics & Probability Letters.

[20]  B. Schweizer,et al.  On Nonparametric Measures of Dependence for Random Variables , 1981 .

[21]  D. Ruppert,et al.  Flexible Copula Density Estimation with Penalized Hierarchical B‐splines , 2013 .

[22]  Christian Genest,et al.  Locally most powerful rank tests of independence for copula models , 2005 .

[23]  Christian Habermann,et al.  Multidimensional Spline Interpolation: Theory and Applications , 2007 .

[24]  Christian Genest,et al.  On the empirical multilinear copula process for count data , 2014, 1407.1200.

[25]  Claudia Czado,et al.  Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas , 2015, J. Multivar. Anal..

[26]  Jakob Gulddahl Rasmussen,et al.  GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models , 2016 .

[27]  Ximing Wu,et al.  Transformation-Kernel Estimation of the Copula Density , 2015 .

[28]  M. Sklar Fonctions de repartition a n dimensions et leurs marges , 1959 .

[29]  Göran Kauermann,et al.  Flexible pair-copula estimation in D-vines using bivariate penalized splines , 2014, Stat. Comput..

[30]  P. Embrechts,et al.  Dependence modeling with copulas , 2007 .