Inter-regional correlation estimators for functional magnetic resonance imaging

Autor: Sophie Achard, Jean-François Coeurjolly, Pierre Lafaye de Micheaux, Hanâ Lbath, Jonas Richiardi

Publikováno v: NeuroImage, Vol 282, Iss , Pp 120388- (2023)

Functional magnetic resonance imaging (fMRI) functional connectivity between brain regions is often computed using parcellations defined by functional or structural atlases. Typically, some kind of voxel averaging is performed to obtain a single temp

Externí odkaz: https://doaj.org/article/811fa31e69654f33938e416a4458d1bc

A concentration inequality for inhomogeneous Neyman–Scott point processes

Autor: Jean-François Coeurjolly, Patricia Reynaud-Bouret

Publikováno v: Statistics & Probability Letters. 148:30-34

In this note, we prove some non-asymptotic concentration inequalities for functionals, called innovations, of inhomogeneous Neyman–Scott point processes, a particular class of spatial point process models. Innovation is a functional built from the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e82de965efd687dee74abeaaf841e042
https://doi.org/10.1016/j.spl.2018.12.003

A convolution type model for the intensity of spatial point processes applied to eye-movement data

Autor: Jean-François Coeurjolly, Francisco Cuevas-Pacheco, Marie-Hélène Descary

Estimating the first-order intensity function in point pattern analysis is an important problem, and it has been approached so far from different perspectives: parametrically, semiparametrically or nonparametrically. Our approach is close to a semipa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91ce06e179022e5419ef5fe39b5fdc0d
http://arxiv.org/abs/2012.09659

Regularized estimation for highly multivariate log Gaussian Cox processes

Autor: Rasmus Waagepetersen, Jean-François Coeurjolly, Achmad Choiruddin, Francisco Cuevas-Pacheco

Publikováno v: Statistics and Computing
Statistics and Computing, Springer Verlag (Germany), 2020, 30 (3), pp.649-662. ⟨10.1007/s11222-019-09911-y⟩
Choiruddin, A, Pacheco, F A C, Coeurjolly, J-F & Waagepetersen, R 2020, ' Regularized estimation for highly multivariate log Gaussian Cox processes ', Statistics and Computing, vol. 30, no. 3, pp. 649–662 . https://doi.org/10.1007/s11222-019-09911-y

Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3905cd69b18329d0d8baa3487adb323
https://hal.archives-ouvertes.fr/hal-02119264/file/1.sparseLGCP.pdf

Is future climate predictable with statistics?

Autor: Adeline Leclercq-Samson, Jean-François Coeurjolly, Aurélien Ribes

Publikováno v: ESAIM: Proceedings and Surveys, Vol 60, Pp 104-113 (2017)

The purpose of this note is to briefly introduce the statistical models and methods used in climate sciences to estimate, from observations, the sensitivity of the Earth's climate to Greenhouse Gases. First the context of climatology is described wit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a2f75da7d10a7bb0a169ab78e97cb96
https://doaj.org/article/94a2a3c374f4444dae6e01deeb0025c4