Výsledky vyhledávání - "Magalie Fromont"

Family-Wise Separation Rates for multiple testing

Autor: Matthieu Lerasle, Patricia Reynaud-Bouret, Magalie Fromont

Publikováno v: Annals of Statistics
Annals of Statistics, Institute of Mathematical Statistics, 2016, 44 (6), pp.2533-2563. ⟨10.1214/15-AOS1418⟩
Annals of Statistics, Institute of Mathematical Statistics, 2016, 44 (6), pp.2533-2563. 〈10.1214/15-AOS1418〉
Annals of Statistics, 2016, 44 (6), pp.2533-2563. ⟨10.1214/15-AOS1418⟩
Ann. Statist. 44, no. 6 (2016), 2533-2563

Starting from a parallel between some minimax adaptive tests of a single null hypothesis, based on aggregation approaches, and some tests of multiple hypotheses, we propose a new second kind error-related evaluation criterion, as the core of an emerg

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c1deed0496898262d5892411f05bf32
https://doi.org/10.1214/15-aos1418

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Surrogate data methods based on a shuffling of the trials for synchrony detection: the centering issue

Autor: Yann Bouret, Mélisande Albert, Patricia Reynaud-Bouret, Magalie Fromont

Publikováno v: Neural Computation
Neural Computation, 2016, 28 (1), pp.2352-2392. ⟨10.1162/NECO_a_00839⟩
Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2016, 28 (1), pp.2352-2392. 〈10.1162/NECO_a_00839〉
Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2016, 28 (1), pp.2352-2392. ⟨10.1162/NECO_a_00839⟩

International audience; We investigate several distribution-free dependence detection procedures, all based on a shuffling of the trials, from a statistical point of view. The mathematical justification of such procedures lies in the bootstrap princi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc51534790b7e979b8661b46baa74c03
https://hal.science/hal-01154918v2/file/NC-appli-revision-fin.pdf

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Model selection by bootstrap penalization for classification

Autor: Magalie Fromont

Publikováno v: Machine Learning
Machine Learning, Springer Verlag, 2007, 66 (2-3), pp.165-207. 〈10.1007/s10994-006-7679-y〉
Machine Learning, 2007, 66 (2-3), pp.165-207. ⟨10.1007/s10994-006-7679-y⟩
Machine Learning, Springer Verlag, 2007, 66 (2-3), pp.165-207. ⟨10.1007/s10994-006-7679-y⟩

International audience; We consider the binary classification problem. Given an i.i.d. sample drawn from the distribution of an chi x {0,1}-valued random pair, we propose to estimate the so-called Bayes classifier by minimizing the sum of the empiric

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a35021dd05478ed814cd236620f16076
https://doi.org/10.1007/s10994-006-7679-y

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Bootstrap and permutation tests of independence for point processes

Autor: Yann Bouret, Patricia Reynaud-Bouret, Mélisande Albert, Magalie Fromont

Publikováno v: The Annals of Statistics
The Annals of Statistics, Institute of Mathematical Statistics, 2015, 43 (6), pp.2537-2564. 〈10.1214/15-AOS1351〉
Annals of Statistics
Annals of Statistics, Institute of Mathematical Statistics, 2015, 43 (6), pp.2537-2564. ⟨10.1214/15-AOS1351⟩
Ann. Statist. 43, no. 6 (2015), 2537-2564
Annals of Statistics, 2015, 43 (6), pp.2537-2564. ⟨10.1214/15-AOS1351⟩

International audience; Motivated by a neuroscience question about synchrony detection in spike train analysis, we deal with the independence testing problem for point processes. We introduce non-parametric test statistics, which are rescaled general

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a44e05e4d6cb6e0d3fcaeb13c86156e
https://hal.archives-ouvertes.fr/hal-01001984/file/Testindpp_Hal_v4.pdf

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The two-sample problem for Poisson processes: adaptive tests with a non-asymptotic wild bootstrap approach

Autor: Patricia Reynaud-Bouret, Béatrice Laurent, Magalie Fromont

Publikováno v: Annals of Statistics
Annals of Statistics, Institute of Mathematical Statistics, 2013, 41 (3), pp.1431-1461. 〈10.1214/13-AOS1114〉
Annals of Statistics, 2013, 41 (3), pp.1431-1461. ⟨10.1214/13-AOS1114⟩
Annals of Statistics, Institute of Mathematical Statistics, 2013, 41 (3), pp.1431-1461. ⟨10.1214/13-AOS1114⟩
Ann. Statist. 41, no. 3 (2013), 1431-1461

Considering two independent Poisson processes, we address the question of testing equality of their respective intensities. We first propose testing procedures whose test statistics are $U$-statistics based on single kernel functions. The correspondi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da14dc74d165978111db4a2ffa96144b
https://hal.archives-ouvertes.fr/hal-00679102/file/RevisionTestEga.pdf

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Adaptive tests of homogeneity for a Poisson process

Autor: Magalie Fromont, Béatrice Laurent, Patricia Reynaud-Bouret

Publikováno v: Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques
Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, Institute Henri Poincaré, 2011, 47 (1), pp.176-213. 〈10.1214/10-aihp367〉
Ann. Inst. H. Poincaré Probab. Statist. 47, no. 1 (2011), 176-213
Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, 2011, 47 (1), pp.176-213. ⟨10.1214/10-aihp367⟩
Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, Institut Henri Poincaré (IHP), 2011, 47 (1), pp.176-213. ⟨10.1214/10-aihp367⟩

Nous proposons de tester l’homogeneite d’un processus de Poisson observe sur un intervalle borne. Nous etablissons tout d’abord des bornes inferieures pour les vitesses de separation uniformes relativement a la norme $\mathbb {L}^{2}$ sur des B

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e9c263cfb8aea6dc8427005fda26537
https://hal.archives-ouvertes.fr/hal-00867074

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Functional classification with margin conditions

Autor: Christine Tuleau, Magalie Fromont

Publikováno v: Learning theory: Proceedings of the 19th Annual Conference (COLT 2006) held at Carnegie Mellon University, Pittsburgh
Learning theory
Learning theory, Jun 2006, Pittsburgh, United States. pp.94-108
Learning theory, Jun 2006, Pittsburgh, United States. Springer, 4005, pp.94-108, 2006, Lecture notes in computer science
Learning Theory ISBN: 9783540352945
COLT

International audience; Let (X,Y) be a X x {0,1}-valued random pair and consider a sample (X-1, Y-1),..., (X-n, Y-n.) drawn from the distribution of (X, Y). We aim at constructing from this sample a classifier that is a function which would predict t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4ba4e4e0f2a9d2496062db0623e4347
https://hal.archives-ouvertes.fr/hal-00457770

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Adaptive tests for periodic signal detection with applications to laser vibrometry

Autor: Céline Lévy-Leduc, Magalie Fromont

Publikováno v: ESAIM: Probability and Statistics
ESAIM: Probability and Statistics, EDP Sciences, 2006, 10, pp.46-75. ⟨10.1051/ps:1006002⟩
ESAIM: Probability and Statistics, 2006, 10, pp.46-75. ⟨10.1051/ps:1006002⟩
ESAIM: Probability and Statistics, EDP Sciences, 2006, 10, pp.46-75. 〈10.1051/ps:1006002〉

Initially motivated by a practical issue in target detection via laser vibrometry, we are interested in the problem of periodic signal detection in a Gaussian fixed design regression framework. Assuming that the signal belongs to some periodic Sobole

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a39f92a9732dd0740cd7e661ffa855d3
https://hal.archives-ouvertes.fr/hal-00457760/document

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Adaptive goodness-of-fit tests in a density model

Autor: Magalie Fromont, Béatrice Laurent

Publikováno v: Annals of Statistics
Annals of Statistics, Institute of Mathematical Statistics, 2006, 34 (2), pp.680-720. ⟨10.1214/009053606000000119⟩
The Annals of Statistics
The Annals of Statistics, IMS, 2006, 34 (2), pp.680-720. 〈10.1214/009053606000000119〉
Annals of Statistics, 2006, 34 (2), pp.680-720. ⟨10.1214/009053606000000119⟩
Ann. Statist. 34, no. 2 (2006), 680-720

Given an i.i.d. sample drawn from a density $f$, we propose to test that $f$ equals some prescribed density $f_0$ or that $f$ belongs to some translation/scale family. We introduce a multiple testing procedure based on an estimation of the $\mathbb{L

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84983bb476dd879157fa79c1ca8bff38
https://hal.archives-ouvertes.fr/hal-00457755

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Model Selection by Bootstrap Penalization for Classification

Autor: Magalie Fromont

Publikováno v: Learning Theory ISBN: 9783540222828
COLT

We consider the binary classification problem. Given an i.i.d. sample drawn from the distribution of an $\mathcal{X}\times\{0,1\}$-valued random pair, we propose to estimate the so-called Bayes classifier by minimizing the sum of the empirical clas

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::093e598d3b740b8b3e6c1dbcf537d2eb
https://doi.org/10.1007/978-3-540-27819-1_20

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