On the Elephant Random Walk with Stops Playing Hide and Seek with the Mittag–Leffler Distribution

Autor: Bernard Bercu

Publikováno v: Journal of Statistical Physics. 189

The aim of this paper is to investigate the asymptotic behavior of the so-called elephant random walk with stops (ERWS). In contrast with the standard elephant random walk, the elephant is allowed to be lazy by staying on his own position. We prove t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bfb8bee834d1439aa3ea7efe906b0d3
https://doi.org/10.1007/s10955-022-02980-w

How to estimate the memory of the Elephant Random Walk

Autor: Bernard Bercu, Lucile Laulin

We introduce an original way to estimate the memory parameter of the elephant random walk, a fascinating discrete time random walk on integers having a complete memory of its entire history. Our estimator is nothing more than a quasi-maximum likeliho

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db2809a9c792bb00baef10cb29af824d
http://arxiv.org/abs/2112.10405

Asymptotic distribution and convergence rates of stochastic algorithms for entropic optimal transportation between probability measures

Autor: Jérémie Bigot, Bernard Bercu

Publikováno v: Annals of Statistics
Annals of Statistics, Institute of Mathematical Statistics, In press
Annals of Statistics, Institute of Mathematical Statistics, 2021, 49 (2), pp.968-987. ⟨10.1214/20-AOS1987⟩

International audience; This paper is devoted to the stochastic approximation of entropically regularized Wasserstein distances between two probability measures, also known as Sinkhorn divergences. The semi-dual formulation of such regularized optima

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eaf69bb2a2560338b0ecfd71c8694dfe
https://doi.org/10.1214/20-aos1987

An Efficient Stochastic Newton Algorithm for Parameter Estimation in Logistic Regressions

Autor: Bruno Portier, Bernard Bercu, Antoine Godichon

Publikováno v: SIAM Journal on Control and Optimization
SIAM Journal on Control and Optimization, Society for Industrial and Applied Mathematics, 2020, 58 (1), pp.348-367. ⟨10.1137/19M1261717⟩

Logistic regression is a well-known statistical model which is commonly used in the situation where the output is a binary random variable. It has a wide range of applications including machine learning, public health, social sciences, ecology and ec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f06403d66061c0a858dd3aef7949663b
https://hal.archives-ouvertes.fr/hal-02488985

Nonparametric recursive estimation of the derivative of the regression function with application to sea shores water quality

Autor: Sami Capderou, Gilles Durrieu, Bernard Bercu

Publikováno v: Statistical Inference for Stochastic Processes
Statistical Inference for Stochastic Processes, 2017, ⟨10.1007/s11203-017-9169-1⟩
Statistical Inference for Stochastic Processes, Springer Verlag, 2019, 22 (1), pp.17-40
Statistical Inference for Stochastic Processes, Springer Verlag, 2019
Statistical Inference for Stochastic Processes, Springer Verlag, 2019, 22 (1), pp.17-40. ⟨10.1007/s11203-017-9169-1⟩

International audience; This paper is devoted to the nonparametric estimation of the derivative of the regression function in a nonparametric regression model. We implement a very efficient and easy to handle statistical procedure based on the deriva

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff4d2381c597fb06214985b21623939d
https://hal.archives-ouvertes.fr/hal-02470351