Výsledky vyhledávání - "Biernacki, C. (Christophe)"

Dealing with missing data in model-based clustering through a MNAR model

Autor: Biernacki, C. (Christophe), Celeux, G. (Gilles), Josse, J. (Julie), Laporte, F. (Fabien)

Publikováno v: CMStatistics 2018-11th International Conference of the ERCIM WG on Computational and Methodological Statistics
CMStatistics 2018-11th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2018, Pise, Italy
CRoNos & MDA 2019-Meeting and Workshop on Multivariate Data Analysis and Software
CRoNos & MDA 2019-Meeting and Workshop on Multivariate Data Analysis and Software, Apr 2019, Limassol, Cyprus

International audience; Since the 90s, model-based clustering is largely used to classify data. Nowadays, with the increase of available data, missing values are more frequent. Traditional ways to deal with them consist to obtain a filled data set, e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::30de97be3c1d975dd80a274997fde64f
https://hal.inria.fr/hal-02103347

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High-dimensional clustering

Autor: Biernacki, C. (Christophe), Maugis, C. (Cathy)

Publikováno v: Choix de modèles et agrégation, Sous la direction de J-J. DROESBEKE, G. SAPORTA, C. THOMAS-AGNAN Edition: Technip.
Choix de modèles et agrégation, Sous la direction de J-J. DROESBEKE, G. SAPORTA, C. THOMAS-AGNAN Edition: Technip., 2017, 9782710811770

International audience; High-dimensional (HD) data sets are now frequent, mostly motivated by technological reasons which concern automation in variable acquisition, cheaper availability of data storage and more powerful standard computers for quick

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::9bb9f3e407f1acd6d00d0695f10d4f62
https://hal.archives-ouvertes.fr/hal-01252673v2/file/JES2014-chap2.pdf

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Variable Clustering in High-Dimensional Linear Regression:The R Package clere

Autor: Yengo, L. (Loïc), Jacques, J. (Julien), Biernacki, C. (Christophe), Canouil, M. (Mickael)

Dimension reduction is one of the biggest challenge in high-dimensional regression models. We recently introduced a new methodology based on variable clustering as a means to reduce dimensionality. We introduce here an R package that implements two e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______4198::75d36323579be119b6d70902613816f0
http://hdl.handle.net/20.500.12210/29337

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CorReg:Préselection de variables en régression linéaire avec fortes corrélations

Autor: Théry, C. (Clément), Biernacki, C. (Christophe), Loridant, G. (Gaétan)

La régression linéaire est pénalisée par l'usage de variables explicatives corrélées, situation fréquente pour les bases de données d'origine industrielleò u les corrélations sont nombreuses et enen a des estimateurs de forte variance. Lemo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______4198::b9aa8041a6d8e54b424712b9836fa1b0
http://hdl.handle.net/20.500.12210/29188

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