23.12.2013 Views

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

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

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

SHOW MORE
SHOW LESS

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

Conclusion <strong>et</strong> perspectives<br />

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

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

Hilbe, J. M. (2009), Logistic regression mo<strong>de</strong>ls, Chapman and Hall.<br />

Hin, H. K. and Huiyong, S. (2006), ‘Structural prepayment risk behavior of the un<strong>de</strong>rlying<br />

mortgages for resi<strong>de</strong>ntial mortgage life insurance in a <strong>de</strong>veloping mark<strong>et</strong>’, Journal of Housing<br />

Economics (15), 257–278.<br />

Hosmer, D. W. and Lemeshow, S. (2000), Applied Logistic Regression, 2nd ed., Wiley.<br />

Kagraoka, Y. (2005), Mo<strong>de</strong>ling insurance surren<strong>de</strong>rs by the negative binomial mo<strong>de</strong>l. Working<br />

Paper 2005.<br />

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

d’Evry Val d’Essonne.<br />

Kim, C. (2005), ‘Mo<strong>de</strong>ling surren<strong>de</strong>r and lapse rates with economic variables’, North American<br />

Actuarial Journal pp. 56–70.<br />

Kim, C. N., Yang, K. H. and Kim, J. (2008), ‘Human <strong>de</strong>cision-making behavior and mo<strong>de</strong>ling<br />

effects’, Decision Support Systems 45, 517–527.<br />

Kuen, S. T. (2005), ‘Fair valuation of participating policies with surren<strong>de</strong>r options and regime<br />

switching’, Insurance : Mathematics and Economics 37, 533–552.<br />

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

Statistics 22(1), 79–86.<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.<br />

Lee, S., Son, Y.-J. and Jin, J. (2008), ‘Decision field theory extensions for behavior mo<strong>de</strong>ling in<br />

dynamic environment using bayesian belief n<strong>et</strong>work’, Information Sciences 178, 2297–2314.<br />

Lefèvre, C. and Utev, S. (1996), ‘Comparing sums of exchangeable Bernoulli random variables’,<br />

J. Appl. Probab. 33(2), 285–310.<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.<br />

Lemmens, A. and Croux, C. (2006), ‘Bagging and boosting classification trees to predict<br />

churn’, Journal of Mark<strong>et</strong>ing Research 134(1), 141–156.<br />

Lindsay, B. and Lesperance, M. (1995), ‘A review of semiparam<strong>et</strong>ric mixture mo<strong>de</strong>ls’, Journal<br />

of Statistical Planning and Inference 47, 29–99.<br />

Lindstrom, M. and Bates, D. (1988), ‘Newton-raphson and em algorithms for linear mixe<strong>de</strong>ffects<br />

mo<strong>de</strong>ls for repeated-measures data’, Journal of the American Statistical Association<br />

83, 1014–1022.<br />

Liu, Y., Chawla, N., Harper, M., Shriberg, E. and Stolcke, A. (2006), ‘A study in machine<br />

learning for unbalanced data for sentence boundary <strong>de</strong>tection in speech.’, Computer Speech<br />

and Language 20(4), 468–494.<br />

182

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!