kdecopula: An R Package for the Kernel Estimation of Bivariate Copula Densities
نویسنده
چکیده
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.
منابع مشابه
Nonparametric estimation of simpli ed vine copula models: comparison of methods
Thomas Nagler*, Christian Schellhase, and Claudia Czado Nonparametric estimation of simpli ed vine copula models: comparison of methods DOI 10.1515/demo-2017-0007 Received December 27, 2016; accepted May 16, 2017 Abstract: In the last decade, simpli ed vine copula models have been an active area of research. They build a high dimensional probability density from the product of marginals densiti...
متن کاملTwo-stage estimation using copula function
Maximum likelihood estimation of multivariate distributions needs solving a optimization problem with large dimentions (to the number of unknown parameters) but two- stage estimation divides this problem to several simple optimizations. It saves significant amount of computational time. Two methods are investigated for estimation consistency check. We revisit Sankaran and Nair's bivari...
متن کاملKernel-type density estimation on the unit interval
We consider kernel-type methods for estimation of a density on [0, 1] which eschew explicit boundary correction. Our starting point is the successful implementation of beta kernel density estimators of Chen (1999). We propose and investigate two alternatives. For the first, we reverse the roles of estimation point x and datapoint Xi in each summand of the estimator. For the second, we provide k...
متن کاملNonparametric multiplicative bias correction for kernel-type density estimation on the unit interval
We consider kernel-type methods for estimation of a density on [0, 1] which eschew explicit boundary correction. Our starting point is the successful implementation of beta kernel density estimators of Chen (1999). We propose and investigate two alternatives. For the first, we reverse the roles of estimation point x and datapoint Xi in each summand of the estimator. For the second, we provide k...
متن کاملProbit Transformation for Nonparametric Kernel Estimation of the Copula Density
Copula modelling has become ubiquitous in modern statistics. Here, the problem of nonparametrically estimating a copula density is addressed. Arguably the most popular nonparametric density estimator, the kernel estimator is not suitable for the unit-square-supported copula densities, mainly because it is heavily a↵ected by boundary bias issues. In addition, most common copulas admit unbounded ...
متن کامل