Výsledky vyhledávání - "[ STAT.OT ] Statistics [stat]/Other Statistics [stat.ML]"

Order selection with confidence for finite mixture models

Autor: Hien D. Nguyen, Daniel Fryer, Geoffrey J. McLachlan

Publikováno v: Journal of the Korean Statistical Society. 52:154-184

The determination of the number of mixture components (the order) of a finite mixture model has been an enduring problem in statistical inference. We prove that the closed testing principle leads to a sequential testing procedure (STP) that allows fo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d7396cb4d1f4fc74ffb1da989ce7c0b
https://doi.org/10.1007/s42952-022-00195-z

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Finite sample inference for empirical Bayesian methods

Autor: Hien Duy Nguyen, Mayetri Gupta

In recent years, Empirical Bayesian (EB) inference has become an attractive approach for estimation in parametric models arising in a variety of real-life problems, especially in complex and high-dimensional scientific applications. However, compared

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d605db881ddd209c3981ba69a2a220e9
http://arxiv.org/abs/2302.14531

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Digitale Werkzeuge im Arbeitsschutz, Bremsen oder Hebel zum Aufbau multidisziplinärer Dynamik?

Autor: Gouvenelle, Cédric, Flora, Maudhuy, Florence, Thorin

Publikováno v: Actes du 12eme colloque EPIQUE 2023
EPIQUE 2023, 12ème colloque de Psychologie Ergonomique
EPIQUE 2023, 12ème colloque de Psychologie Ergonomique, ARPEGE, Jul 2023, Paris Institut Biomédical des Armées, Val de Grâce, France

International audience; Die Einführung digitaler Plattformen hat die Arbeitsgesundheit revolutioniert, indem sie den überbetrieblichen Arbeitsschutz- und Präventionsdiensten (SPSTI) die Möglichkeit gegeben hat, Datenbanken zu erwerben, um Fachleu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2885::b5ce73f2a74533cd99a80e3deb84bfbd
https://hal.science/hal-04159517/document

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Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains

Autor: Duchemin, Quentin, De Castro, Yohann, Lacour, Claire

Publikováno v: Bernoulli
Bernoulli, Bernoulli Society for Mathematical Statistics and Probability, In press

We prove a new concentration inequality for U-statistics of order two for uniformly ergodic Markov chains. Working with bounded and $\pi$-canonical kernels, we show that we can recover the convergence rate of Arcones and Gin{\'e} who proved a concent

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::014e2e5ad0dd57b85d22e95a847ff1db
https://doi.org/10.3150/22-bej1485

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Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models

Autor: Balelli, Irene, Sportisse, Aude, Cremonesi, Francesco, Mattei, Pierre-Alexandre, Lorenzi, Marco

Federated learning allows for the training of machine learn- ing models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processing, including strate- gies for handling missing data, remains a major

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a23b7730ca31364d21d69fdb0648cb89
https://hal.science/hal-04069795/document

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deep PACO: Combining statistical models with deep learning for exoplanet detection and characterization in direct imaging at high contrast

Autor: Flasseur, Olivier, Bodrito, Théo, Mairal, Julien, Ponce, Jean, Langlois, Maud, Lagrange, Anne-Marie

Direct imaging is an active research topic in astronomy for the detection and the characterization of young sub-stellar objects. The very high contrast between the host star and its companions makes the observations particularly challenging. In this

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae0d3c83a16b6d8976a817beab255814
http://arxiv.org/abs/2303.02461

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Are labels informative in semi-supervised learning?Estimating and leveraging the missing-data mechanism

Autor: Sportisse, Aude, Schmutz, Hugo, Humbert, Olivier, Bouveyron, Charles, Mattei, Pierre-Alexandre

Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of “informative” labels, which occur when some classes are more likely to be labeled than ot

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ab823709f14356e2dc0634ec0602f6c5
https://hal.science/hal-03982898

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Interval estimation, point estimation, and null hypothesis significance testing calibrated by an estimated posterior probability of the null hypothesis

Autor: Bickel, David R.

Publikováno v: Communications in Statistics - Theory and Methods. 52:763-787

Much of the blame for failed attempts to replicate reports of scientific findings has been placed on ubiquitous and persistent misinterpretations of the p value. An increasingly popular solution is to transform a two-sided p value to a lower bound on

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db90fc64a0b1b3e970bf351d4fd6b44a
https://doi.org/10.1080/03610926.2021.1921805

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Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions

Autor: Maxime Taillardat, Anne-Laure Fougères, Philippe Naveau, Raphaël de Fondeville

Publikováno v: International Journal of Forecasting
International Journal of Forecasting, In press, ⟨10.1016/j.ijforecast.2022.07.003⟩
International Journal of Forecasting, 2022, 39 (3), pp.1448-1459. ⟨10.1016/j.ijforecast.2022.07.003⟩
International Journal of Forecasting, 39 (3)
International Journal of Forecasting, 2022, ⟨10.1016/j.ijforecast.2022.07.003⟩

Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on extreme events, and biased conclusions are readily made. In this context, classical verification

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee56b95ca53fe35bb9b1b677997b05f1

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Uniform Risk Bounds for Learning with Dependent Data Sequences

Autor: Lauer, Fabien

This paper extends standard results from learning theory with independent data to sequences of dependent data. Contrary to most of the literature, we do not rely on mixing arguments or sequential measures of complexity and derive uniform risk bounds

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9898a5f56044d0eaee4fbaea38f24130

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