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Mélanges de GLMs et nombre de composantes : application ... - Scor

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BIBLIOGRAPHIE<br />

Garel, B. (2007), ‘Recent asymptotic results in testing for mixtures’, Computational Statistics<br />

and Data Analysis 51, 5295–5304. 115<br />

Grun, B. and Leisch, F. (2004), Bootstrapping finite mixture mo<strong>de</strong>ls, compstat’2004 symposium<br />

edn, Physica Verlag, Hei<strong>de</strong>lberg. 147<br />

Grun, B. and Leisch, F. (2007), ‘Fitting finite mixtures of generalized linear regressions in r’,<br />

Computational Statistics and Data Analysis 51, 5247–5252. 147<br />

Grun, B. and Leisch, F. (2008), I<strong>de</strong>ntifiability of finite mixtures of multinomial logit mo<strong>de</strong>ls<br />

with varying and fixed effects, Technical Report 24, Department of Statistics, University of<br />

Munich. 147<br />

Hai Xan, W., Bin, L., Quan bing, Z. and Sui, W. (2004), ‘Estimation for the number of components<br />

in a mixture mo<strong>de</strong>l using stepwise split-and-merge em algorithm’, Pattern Recognition<br />

L<strong>et</strong>ters 25, 1799–1809. 115<br />

Hathaway, R. (1986), ‘A constrained em algorithm for univariate normal mixtures’, Journal<br />

of Statistical Computation and Simulation 23(3), 211–230. 119<br />

Keribin, C. (1999), Tests <strong>de</strong> modèles par maximum <strong>de</strong> vraisemblance, PhD thesis, Université<br />

d’Evry Val d’Essonne. 116, 117<br />

Kullback, S. and Leibler, R. (1951), ‘On information and sufficiency’, The Annals of Mathematical<br />

Statistics 22(1), 79–86. 104, 105<br />

Lebarbier, E. and Mary-Huard, T. (2004), Le critère bic : fon<strong>de</strong>ments théoriques <strong>et</strong> interprétation,<br />

Technical Report 5315, INRIA. 111<br />

Leisch, F. (2008), Mo<strong>de</strong>lling background noise in finite mixtures of generalized linear regression<br />

mo<strong>de</strong>ls, Technical Report 37, Department of Statistics, University of Munich. 147<br />

Mallows, C. (1974), ‘Some comments on cp’, Technom<strong>et</strong>rics 15, 661–675. 104<br />

Massart, P. (2007), Concentration inequalities and mo<strong>de</strong>l selection. Ecole d’été <strong>de</strong> Probabilités<br />

<strong>de</strong> Saint-Flour 2003., Lecture Notes in Mathematics, Springer. 135, 138, 139, 140<br />

McCullagh, P. and Nel<strong>de</strong>r, J. A. (1989), Generalized linear mo<strong>de</strong>ls, 2nd ed., Chapman and<br />

Hall. 147, 150<br />

McLachlan, G. and Peel, D. (2000), Finite Mixture Mo<strong>de</strong>ls, Wiley Series In Porbability and<br />

Statistics. 116, 122, 133, 134<br />

Mun, E.-Y., von Eye, A., Bates, M. and Vaschillo, E. (2008), ‘Finding groups using mo<strong>de</strong>lbased<br />

cluster analysis : h<strong>et</strong>erogeneous emotional self-regulatory processes and heavy alcohol<br />

use risk’, Developmental Psychology 44, 481–495. 111<br />

Nishii, R. (1988), ‘Maximum likelihood principle and mo<strong>de</strong>l selection when the true mo<strong>de</strong>l is<br />

unspecified’, Journal of Multivariate Analysis (27), 392–403. 106, 107, 113, 134, 137<br />

Ohlson, E. and Johansson, B. (2010), Non-Life Insurance Pricing with Generalized Linear<br />

Mo<strong>de</strong>ls, Springer. 147<br />

173

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