Alexis DERUMIGNY

Grade : Doctorant contractuel GENES

Mail : alexis.derumigny[arrowbase]ensae.fr

ResearchEducationJobsPublicationsTeachingWork in progressOtherLinks

Research Interests

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Dependence modeling, copulas, high-dimensional statistics, kernel smoothing, statistical modeling of conditional distributions


Biography

Education

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  • 2016 - Present : PhD at CREST under the joint supervision of Alexandre Tsybakov and Jean-David Fermanian
  • 2013 - 2016 : M.Sc. in Probability, Statistics, Economics and Finance at ENSAE ParisTech
  • 2011 - 2013 : Preparatory class for entrance to graduate schools ("Grandes Écoles"), Lycée Henri IV, MPSI-MP*

Jobs

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  • May 2016 - September 2016 : Graduate Research Intern, CREST
  • June 2015 - January 2016 : Quantitative Analyst Intern, Meteo Protect


Publications

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Journal articles 

 

Abstract: " It is shown how the problem of estimating conditional Kendall’s tau can be rewritten as a classification task. Conditional Kendall’s tau is a conditional dependence parameter that is a characteristic of a given pair of random variables. The goal is to predict whether the pair is concordant (value of 1) or discordant (value of -1) conditionally on some covariates. The consistency and the asymptotic normality of a family of penalized approximate maximum likelihood estimators is proven, including the equivalent of the logit and probit regressions in our framework. Specific algorithms are detailed, adapting usual machine learning techniques, including nearest neighbors, decision trees, random forests and neural networks, to the setting of the estimation of conditional Kendall’s tau. Finite sample properties of these estimators and their sensitivities to each component of the data-generating process are assessed in a simulation study. Finally, all these estimators are applied to a dataset of European stock indices.  "

 

Abstract: " Extending the results of Bellec, Lecué and Tsybakov to the setting of sparse high-dimensional linear regression with unknown variance, we show that two estimators, the Square-Root Lasso and the Square-Root Slope can achieve the optimal minimax prediction rate, which is (s/n) log(p/s), up to some constant, under some mild conditions on the design matrix. Here, n is the sample size, p is the dimension and is the sparsity parameter. We also prove optimality for the estimation error in the lq-norm, with q in [1,2] for the Square-Root Lasso, and in the l2 and sorted l1 norms for the Square-Root Slope. Both estimators are adaptive to the unknown variance of the noise. The Square-Root Slope is also adaptive to the sparsity s of the true parameter. Next, we prove that any estimator depending on s which attains the minimax rate admits an adaptive to s version still attaining the same rate. We apply this result to the Square-root Lasso. Moreover, for both estimators, we obtain valid rates for a wide range of confidence levels, and improved concentration properties as in [Bellec, Lecué and Tsybakov, 2017] where the case of known variance is treated. Our results are non-asymptotic. "

 

Abstract: " We discuss the so-called "simplifying assumption" of conditional copulas in a general framework. We introduce several tests of the latter assumption for non- and semiparametric copula models. Some related test procedures based on conditioning subsets instead of point-wise events are proposed. The limiting distribution of such test statistics under the null are approximated by several bootstrap schemes, most of them being new. We prove the validity of a particular semiparametric bootstrap scheme. Some simulations illustrate the relevance of our results. "

 



Teaching

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2018 - 2019 :
  • Probability Theory ; Numerical Analysis (ENSAE 1st year)
  • C++, Mathematical Statistics 1 (ENSAE 2nd year)
  • Time Series ; Financial Econometrics (ENSAE 3rd year)
 
2017 - 2018 :
  • Probability Theory ; Numerical Analysis (ENSAE 1st year)
  • C++ (ENSAE 2nd year)
  • Time Series ; Financial Econometrics (ENSAE 3rd year)
 
2016 - 2017 :
  • Analysis and Topology ; Convex Optimization ; Numerical Analysis (ENSAE 1st year)
  • Financial Econometrics (ENSAE 3rd year)

Work in progress

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Preprints and submitted articles 

Abstract: " We study nonparametric estimators of conditional Kendall's tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic bounds with explicit constants, that hold with high probabilities. We provide "direct proofs" of the consistency and the asymptotic law of conditional Kendall's tau. A simulation study evaluates the numerical performance of such nonparametric estimators."

 

Abstract: "Conditional Kendall's tau is a measure of dependence between two random variables, conditionally on some covariates. We assume a regression-type relationship between conditional Kendall's tau and some covariates, in a parametric setting with a large number of transformations of a small number of regressors. This model may be sparse, and the underlying parameter is estimated through a penalized criterion. We prove non-asymptotic bounds with explicit constants that hold with high probabilities. We derive the consistency of a two-step estimator, its asymptotic law and some oracle properties. Some simulations and applications to real data conclude the paper."

 


Other

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Conferences & communications

 

2018:

 

2017:

 

2016:

  • Inference of elliptical copula generators, with Jean-David Fermanian, Invited speaker at the 9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016, Seville, Spain, 9-11 December 2016)


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My LinkedIn profile : www.linkedin.com/in/alexis-derumigny

My personal website : https://alexisderumigny.wordpress.com/

 


"Le centre de la Recherche en Économie et Statistique ne peut être tenu responsable pénalement des infractions aux lois que pourrait contenir cette page personnelle qui est sous la responsabilité de son auteur."