3 citations · 11 across the 15 of their papers we have counts for
17 papers
Deep-testing: the case of dependence detection
Gery Geenens, Pierre Lafaye de Micheaux, Ivan Muyun Zou
Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can be transferred to hypothesis te…
The empirical discrete copula process
Gery Geenens, Ivan Kojadinovic, Tommaso Martini
This paper develops a general inferential framework for discrete copulas on finite supports in any dimension. The copula of a multivariate discrete distribution is defined as Csisz…
On the differentiability of -projections in the discrete finite case
Gery Geenens, Ivan Kojadinovic, Tommaso Martini
In the case of finite measures on finite spaces, we state conditions under which ϕ- projections are continuously differentiable. When the set on which one wishes to ϕ- project is c…
(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.
Towards a universal representation of statistical dependence
Gery Geenens
Dependence is undoubtedly a central concept in statistics. Though, it proves difficult to locate in the literature a formal definition which goes beyond the self-evident 'dependenc…
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…
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…
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…
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…
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…
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…
Mellin-Meijer-kernel density estimation on
Gery Geenens
Nonparametric kernel density estimation is a very natural procedure which simply makes use of the smoothing power of the convolution operation. Yet, it performs poorly when the den…
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…
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…
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…
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…
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…