G
Gery Geenens
Researcher at University of New South Wales
Publications - 35
Citations - 466
Gery Geenens is an academic researcher from University of New South Wales. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 7, co-authored 33 publications receiving 326 citations. Previous affiliations of Gery Geenens include University of Melbourne & Université catholique de Louvain.
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Curse of dimensionality and related issues in nonparametric functional regression
TL;DR: In this article, the authors consider the problem of the curse of infinite dimensionality in functional regression and propose to measure the proximity between two elements of the infinite dimensional functional space via a semi-metric, which could prevent those estimators from suffering from the curse.
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Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation
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Probit transformation for nonparametric kernel estimation of the copula density
TL;DR: In this paper, a kernel-type copula density estimator is proposed, which is based on the idea of transforming the uniform marginals of the copula densities into normal distributions via the probit function, estimating the density in the transformed domain, which can be accomplished without boundary problems, and obtaining an estimate of the Copula density through back-transformation.
Posted Content
Probit transformation for nonparametric kernel estimation of the copula density
TL;DR: It is shown that the kernel-type copula density estimator is very good and easy to implement estimators, fixing boundary issues in a natural way and able to cope with unbounded copula densities, if combined with local likelihooddensity estimation methods.
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Probit Transformation for Kernel Density Estimation on the Unit Interval
TL;DR: In this article, the authors proposed to transform the variable of interest into a variable whose density has unconstrained support, estimating that density, and obtaining an estimate of the density of the original variable through back-transformation.