References of "Ley, Christophe"
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See detailWhat makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences
Ley, Christophe; Bordas, Stéphane UL

E-print/Working paper (2017)

Data Science is today one of the main buzzwords be it in business, industrial or academic settings. Machine learning, experimental design, data-driven modelling are all, undoubtedly, rising disciplines if ... [more ▼]

Data Science is today one of the main buzzwords be it in business, industrial or academic settings. Machine learning, experimental design, data-driven modelling are all, undoubtedly, rising disciplines if one goes by the soaring number of research papers and patents appearing each year. The prospect of becoming a ``Data Scientist'' appeals to many. A discussion panel organised as part of the European Data Science Conference (European Association for Data Science (EuADS)) asked the question: ``What makes Data Science different?'' In this paper we give our own, personal and multi-facetted view on this question, from an engineering and a statistics perspective. In particular, we compare Data Science to Statistics and discuss the connection between Data Science and Computational Science. [less ▲]

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See detailEfficient inference about the tail weight in multivariate Student t distributions
Neven, Anouk UL; Ley, Christophe

in Journal of Statistical Planning & Inference (2015)

We propose a new testing procedure about the tail weight parameter of multivariate Student t distributions by having recourse to the Le Cam methodology. Our test is asymptotically as efficient as the ... [more ▼]

We propose a new testing procedure about the tail weight parameter of multivariate Student t distributions by having recourse to the Le Cam methodology. Our test is asymptotically as efficient as the classical likelihood ratio test, but outperforms the latter by its flexibility and simplicity: indeed, our approach allows to estimate the location and scatter nuisance parameters by any root-n consistent estimators, hereby avoiding numerically complex maximum likelihood estimation. The finite-sample properties of our test are analyzed in a Monte Carlo simulation study, and we apply our method on a financial data set. We conclude the paper by indicating how to use this framework for efficient point estimation. Keywords and Phrases: efficient testing procedures; likelihood ratio test; local asymptotic normality; Student t distribution; tail weight [less ▲]

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See detailMaximum likelihood characterization of distributions
Duerinckx, Mitia; Ley, Christophe; Swan, Yvik UL

in Bernoulli (2014), 20(2), 775-802

A famous characterization theorem due to C. F. Gauss states that the maximum likelihood estimator (MLE) of the parameter in a location family is the sample mean for all samples of all sample sizes if and ... [more ▼]

A famous characterization theorem due to C. F. Gauss states that the maximum likelihood estimator (MLE) of the parameter in a location family is the sample mean for all samples of all sample sizes if and only if the family is Gaussian. There exist many extensions of this result in diverse directions, most of them focussing on location and scale families. In this paper we propose a unified treatment of this literature by providing general MLE characterization theorems for one-parameter group families (with particular attention on location and scale parameters). In doing so we provide tools for determining whether or not a given such family is MLE-characterizable, and, in case it is, we define the fundamental concept of minimal necessary sample size at which a given characterization holds. Many of the cornerstone references on this topic are retrieved and discussed in the light of our findings, and several new characterization theorems are provided. Of particular interest is that one part of our work, namely the introduction of so-called equivalence classes for MLE characterizations, is a modernized version of Daniel Bernoulli's viewpoint on maximum likelihood estimation. [less ▲]

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See detailParametric Stein operators and variance bounds
Ley, Christophe; Swan, Yvik UL

E-print/Working paper (2013)

Stein operators are differential operators which arise within the so-called Stein's method for stochastic approximation. We propose a new mechanism for constructing such operators for arbitrary ... [more ▼]

Stein operators are differential operators which arise within the so-called Stein's method for stochastic approximation. We propose a new mechanism for constructing such operators for arbitrary (continuous or discrete) parametric distributions with continuous dependence on the parameter. We provide explicit general expressions for location, scale and skewness families. We also provide a general expression for discrete distributions. For specific choices of target distributions (including the Gaussian, Gamma and Poisson) we compare the operators hereby obtained with those provided by the classical approaches from the literature on Stein's method. We use properties of our operators to provide upper and lower variance bounds (only lower bounds in the discrete case) on functionals h(X) of random variables X following parametric distributions. These bounds are expressed in terms of the first two moments of the derivatives (or differences) of h. We provide general variance bounds for location, scale and skewness families and apply our bounds to specific examples (namely the Gaussian, exponential, Gamma and Poisson distributions). The results obtained via our techniques are systematically competitive with, and sometimes improve on, the best bounds available in the literature. [less ▲]

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See detailStein's density approach and information inequalities
Ley, Christophe; Swan, Yvik UL

in Electronic Communications in Probability (2013), 18(7), 1--14

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See detailOptimal rank-based inference for spherical location
Ley, Christophe; Swan, Yvik UL; Thiam, Baba et al

in Statistica Sinica (2013), 23

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See detailLocal Pinsker inequalities via Stein's discrete density approach
Ley, Christophe; Swan, Yvik UL

in IEEE Transactions on Information Theory (2013), 59(9), 5584-4491

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See detailA Stochastic Analysis of Table Tennis
Dominicy, Yves; Ley, Christophe; Swan, Yvik UL

in Brazilian Journal of Probability and Statistics (2013), to appear

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See detailEfficient ANOVA for directional data
Ley, Christophe; Swan, Yvik UL; Verdebout, Thomas

E-print/Working paper (2012)

In this paper we tackle the ANOVA problem for directional data (with particular emphasis on geological data) by having recourse to the Le Cam methodology usually reserved for linear multivariate analysis ... [more ▼]

In this paper we tackle the ANOVA problem for directional data (with particular emphasis on geological data) by having recourse to the Le Cam methodology usually reserved for linear multivariate analysis. We construct locally and asymptotically most stringent parametric tests for ANOVA for directional data within the class of rotationally symmetric distributions. We turn these parametric tests into semi-parametric ones by (i) using a studentization argument (which leads to what we call pseudo-FvML tests) and by (ii) resorting to the invariance principle (which leads to efficient rank-based tests). Within each construction the semi-parametric tests inherit optimality under a given distribution (the FvML distribution in the first case, any rotationally symmetric distribution in the second) from their parametric antecedents and also improve on the latter by being valid under the whole class of rotationally symmetric distributions. Asymptotic relative efficiencies are calculated and the finite-sample behaviour of the proposed tests is investigated by means of a Monte Carlo simulation. We conclude by applying our findings on a real-data example involving geological data. [less ▲]

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See detailOn a connection between Stein characterizations and Fisher information
Ley, Christophe; Swan, Yvik UL

Report (2011)

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