{"id":1092,"date":"2020-02-12T11:33:19","date_gmt":"2020-02-12T10:33:19","guid":{"rendered":"http:\/\/www.bordeaux-population-health.center\/biostatistics-conferences-2020\/"},"modified":"2026-02-05T09:39:57","modified_gmt":"2026-02-05T08:39:57","slug":"biostatistics-conferences-2020","status":"publish","type":"post","link":"https:\/\/www.bordeaux-population-health.center\/fr\/biostatistics-conferences-2020\/","title":{"rendered":"Biostatistics Conferences 2020"},"content":{"rendered":"

These events have been organised by Bordeaux Population Health – UMR 1219 and the Public Health Department of the university of Bordeaux.<\/p>\n

\n

Thursday February 13, 2020 <\/strong>– 1 pm to 2 pm<\/strong><\/p>\n

Bayesian sparsity for statistical learning in high dimensions: applications to medical imaging<\/strong><\/p>\n

Amphi Pierre-Alexandre Louis, ISPED
\nCampus Carreire, 146 rue L\u00e9o Saignat, 33076 Bordeaux
\nfree entrance, no registration<\/em><\/p>\n

Speaker: Charles Bouveyron\u00a0 <\/strong>(https:\/\/math.unice.fr\/~cbouveyr\/)<\/a><\/p>\n

Abstract:<\/p>\n

Although the ongoing digital revolution in fields such as chemometrics, genomics or personalized medicine gives hope for considerable progress in these areas, it also provides more and more high-dimensional data to analyze and interpret. A common usual task in those fields is discriminant analysis, which however may suffer from the high dimensionality of the data. The recent advances, through subspace classifica- tion or variable selection methods, allowed to reach either excellent classification performances or useful visualizations and interpretations. Obviously, it is of great interest to have both excellent classification accuracies and a meaningful variable selection for interpretation. This work addresses this issue by intro- ducing a subspace discriminant analysis method which performs a class-specific variable selection through Bayesian sparsity. The resulting classification methodology is called sparse high-dimensional discrimi- nant analysis (sHDDA). Contrary to most sparse methods which are based on the Lasso, sHDDA relies on a Bayesian modeling of the sparsity pattern and avoids the painstaking and sensitive cross-validation of the sparsity level. The main features of sHDDA are illustrated on simulated and real-world data. In particular, we propose an exemplar application to cancer characterization based on medical imaging using radiomic feature extraction is in particular proposed.<\/em>
\n[F. Orlhac, P.-A. Mattei, C. Bouveyron and N. Ayache, Class-specific Variable Selection in High-Dimensional Discriminant Analysis through Bayesian Sparsity, Journal of Chemometrics, vol. 33, pp. e3097, 2019]
\n[C. Bouveyron, P. Latouche and P.-A. Mattei, Bayesian Variable Selection for Globally Sparse Probabilistic PCA, Electronic Journal of Statistics, vol. 12(2), pp. 3036-3070, 2018]<\/p>\n

Next Biostatistics Conferences<\/strong>:<\/p>\n

\n– February 18: J\u00e9r\u00e9mie Lespinasse<\/strong> (5 pm to 6 pm Salle Chastang \u00e0 l’ISPED – campus Carreire) on \u00ab\u00a0mod\u00e9lisation de la s\u00e9quence temporelle des atteintes dans la cohorte MEMENTO par mod\u00e8le mixte multivari\u00e9 avec recalage temporel<\/strong>\u00ab\u00a0
\n– March 5: Arnaud Gloaqen<\/strong> (1 pm to 2pm Amphi Louis \u00e0 l\u2019ISPED – campus de Carreire)
\n– April 23: Lynn Lin<\/strong> (1 pm to 2pm Amphi Louis \u00e0 l\u2019ISPED – campus de Carreire)
\n– April 30: Christopher Foley<\/strong> (1 pm to 2pm Amphi Louis \u00e0 l\u2019ISPED – campus de Carreire)
\n– June 4: Paul Kirk<\/strong> (1 pm to 2pm Amphi Louis \u00e0 l\u2019ISPED – campus de Carreire)
\n– June 25: Stacia M DeSantis<\/strong> (1 pm to 2pm Amphi Louis \u00e0 l\u2019ISPED – campus de Carreire)<\/p>\n

All our conferences in this section: https:\/\/www.bordeaux-population-health.center\/en\/events\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

These events have been organised by Bordeaux Population Health – UMR 1219 and the Public Health Department of the university of Bordeaux. Thursday February 13, 2020 – 1 pm to 2 pm Bayesian sparsity for statistical learning in high dimensions: applications to medical imaging Amphi Pierre-Alexandre Louis, ISPED Campus Carreire, 146 rue L\u00e9o Saignat, 33076 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[58],"tags":[],"class_list":["post-1092","post","type-post","status-publish","format-standard","hentry","category-evenement"],"acf":[],"yoast_head":"\nBiostatistics Conferences 2020 | Bordeaux population health<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.bordeaux-population-health.center\/fr\/biostatistics-conferences-2020\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Biostatistics Conferences 2020 | Bordeaux population health\" \/>\n<meta property=\"og:description\" content=\"These events have been organised by Bordeaux Population Health – UMR 1219 and the Public Health Department of the university of Bordeaux. 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