activity
20122026
most citedAn essay on copula modelling for discrete random vectors; or how to pour new wine into old bottles

3 citations · 11 across the 15 of their papers we have counts for

collaborators
Showing stat.MEShow all

12 papers · 1 filter

stat.ME2023

(Re-)reading Sklar (1959) -- A personal view on Sklar's theorem

Gery Geenens

Some personal thoughts on Sklar's theorem and copulas after reading the original paper (Sklar, 1959) in French.

stat.ME2022

Hellinger-Bhattacharyya cross-validation for shape-preserving multivariate wavelet thresholding

Carlos Aya-Moreno, Gery Geenens, Spiridon Penev

The benefits of the wavelet approach for density estimation are well established in the literature, especially when the density to estimate is irregular or heterogeneous in smoothn…

stat.ME2021

Statistical depth in abstract metric spaces

Gery Geenens, Alicia Nieto-Reyes, Giacomo Francisci

The concept of depth has proved very important for multivariate and functional data analysis, as it essentially acts as a surrogate for the notion a ranking of observations which i…

stat.ME2019★ 3 cited

An essay on copula modelling for discrete random vectors; or how to pour new wine into old bottles

Gery Geenens

Copulas have now become ubiquitous statistical tools for describing, analysing and modelling dependence between random variables. Sklar's theorem, "the fundamental theorem of copul…

stat.ME2018★ 1 cited

The Hellinger Correlation

Gery Geenens, Pierre Lafaye de Micheaux

In this paper, the defining properties of a valid measure of the dependence between two random variables are reviewed and complemented with two original ones, shown to be more fund…

stat.ME2017

A nonparametric copula approach to conditional Value-at-Risk

Gery Geenens, Richard Dunn

Value-at-Risk and its conditional allegory, which takes into account the available information about the economic environment, form the centrepiece of the Basel framework for the e…

stat.ME2017

Shape-preserving wavelet-based multivariate density estimation

Carlos Aya Moreno, Gery Geenens, Spiridon Penev

Wavelet estimators for a probability density f enjoy many good properties, however they are not "shape-preserving" in the sense that the final estimate may not be non-negative or i…

stat.ME2017

Robust analysis of second-leg home advantage in UEFA football through better nonparametric confidence intervals for binary regression functions

Gery Geenens, Thomas Cuddihy

In international football (soccer), two-legged knockout ties, with each team playing at home in one leg and the final outcome decided on aggregate, are common. Many players, manage…

stat.ME2016★ 3 cited

Local-likelihood transformation kernel density estimation for positive random variables

Gery Geenens, Craig Wang

The kernel estimator is known not to be adequate for estimating the density of a positive random variable X. The main reason is the well-known boundary bias problems that it suffer…

stat.ME2014★ 3 cited

Probit transformation for nonparametric kernel estimation of the copula density

Gery Geenens, Arthur Charpentier, Davy Paindaveine

Copula modelling has become ubiquitous in modern statistics. Here, the problem of nonparametrically estimating a copula density is addressed. Arguably the most popular nonparametri…

stat.ME2013

Probit transformation for kernel density estimation on the unit interval

Gery Geenens

Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well known boundary bias issues a conventional kernel densit…

stat.ME2012

A Nonparametric Measure of Local Association for two-way Contingency Tables

Francis K. C. Hui, Gery Geenens

In contingency table analysis, the odds ratio is a commonly applied measure used to summarize the degree of association between two categorical variables, say R and S. Suppose now…