References - Alexandre Lourme

Université de Bordeaux - Collège DSPEG - Master 2 Finance Quantitative & Actuariat - Automne 2017. Scoring Appliqué à la Détection du Risque ... of Hirotugu Akaike, pages 199–213. Springer ... R package version 0.9-2, URL http://CRAN.
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Université de Bordeaux - Collège DSPEG - Master 2 Finance Quantitative & Actuariat - Automne 2017

Scoring Appliqué à la Détection du Risque

References [1] Hirotogu Akaike. Information theory and an extension of the maximum likelihood principle. In Selected Papers of Hirotugu Akaike, pages 199–213. Springer, 1998. [2] Jeffrey L Andrews and Paul D Mcnicholas. Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions. Statistics and Computing, 22(5):1021–1029, 2012. [3] Jeffrey L Andrews, Paul D McNicholas, and Maintainer Jeffrey L Andrews. Package ‘teigen’. 2015. [4] Mireille Bardos. Analyse discriminante: application au risque et scoring financier. Dunod, 2001. [5] Laurent Bergé, Charles Bouveyron, and Stéphane Girard. HDclassif: An R package for model-based clustering and discriminant analysis of high-dimensional data. Journal of Statistical Software, 46(6):1–29, 2012. [6] Przemysław Biecek, Ewa Szczurek, Martin Vingron, and Jerzy Tiuryn. The R package bgmm: Mixture modeling with uncertain knowledge. Journal of Statistical Software, 47(3):1–32, 2012. [7] Christophe Biernacki, Thibault Deregnaucourt, and Vincent Kubicki. Model-based clustering with mixed/missing data using the new software mixtcomp. In CMStatistics 2015 (ERCIM 2015), 2015. [8] Charles BOUVEYRON. Hdclassif: an r package for model-based classification of high-dimensional data. Talk, Université Paris, 1, 2012. [9] Anderson Rodrigo da Silva. biotools, Tools for Biometry and Applied Statistics in Agricultural Science, 2015. [10] MODAL Team INRIA Lille Nord Europe. MixtComp MOdels for Data Analysis and Learning. https:// modal.lille.inria.fr/wikimodal/doku.php?id=mixtcomp, 2015. [11] Chris Fraley and Adrian E. Raftery. Model-based clustering, discriminant analysis and density estimation. Journal of the American Statistical Association, 97:611–631, 2002. [12] Chris Fraley, Adrian E. Raftery, Thomas Brendan Murphy, and Luca Scrucca. mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation, 2012. [13] Alan Genz, Frank Bretz, Tetsuhisa Miwa, Xuefei Mi, Friedrich Leisch, Fabian Scheipl, and Torsten Hothorn. mvtnorm: Multivariate normal and t distributions. R package version 0.9-2, URL http://CRAN. R-project. org/package= mvtnorm, 2008. [14] Gérard Govaert. Data analysis. John Wiley & Sons, 2013. [15] Selcuk Korkmaz, Dincer Goksuluk, and Gokmen Zararsiz. Mvn: An r package for assessing multivariate normality. The R Journal, 6(2):151–162, 2014. [16] Rémi Lebret, Serge Iovleff, Florent Langrognet, Christophe Biernacki, Gilles Celeux, and Gérard Govaert. Rmixmod: the r package of the model-based unsupervised, supervised and semi-supervised classification mixmod library. Journal of Statistical Software, pages In–press, 2015. [17] Remi Lebret, Serge Iovleff, Florent Langrognet, Maintainer Remi Lebret, and LinkingTo Rcpp. Package ‘rmixmod’. 2012. [18] PD McNicholas, KR Jampani, AF McDaid, TB Murphy, and L Banks. pgmm: Parsimonious gaussian mixture models. R package version, 1:24–47, 2011. [19] Gilbert Saporta. Probabilités, analyse des données et statistique. Editions Technip, 2011. [20] Gideon Schwarz et al. Estimating the dimension of a model. The annals of statistics, 6(2):461–464, 1978.

A. Lourme, Faculté d’économie, gestion & AES, Université de Bordeaux http://alexandrelourme.free.fr

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