TH`ESE TITRESURPLUSIEURSLIGNES,SURPLUSIEURSLIGNES

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I.S.F.A. Ecole Doctorale Sciences ´´ Economiques et de Gestion

TITRE SUR PLUSIEURS LIGNES, SUR PLUSIEURS LIGNES

TH` ESE

pr´esent´ee et soutenue publiquement le Date

pour l’obtention du

Doctorat de l’Universit´ e Claude Bernard Lyon I

(math´ematiques appliqu´ees)

par

truc Bidulle

Composition du jury

Rapporteurs : trucBidulle, Professeur `a trucBidulle, Professeur `a

Examinateurs : trucBidulle, Professeur à trucBidulle, Professeur à trucBidulle, Professeur à trucBidulle, Professeur à

Laboratoire Science Actuarielle Financi`ere — EA 2429

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Remerciements

Je voudrais commencer par remercier

i

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ii

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À truc

iii

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iv

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Résumé

title

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Mots-clés:bidulle, truc

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Abstract

title

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Keywords: bidulle truc

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Table des matières

Remerciements i

Résumé v

Tables des matières ix

Introduction générale

Introduction 3

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Principaux résultats 5

ABC

Chapitre 1 titre court 9

Bibliography . . . 9 Appendix . . . 10

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Table des matières

DEF

Conclusion

Conclusion et perspectives 19

Bibliographie 21

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Introduction générale

1

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Introduction

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R Development Core Team (2010); Fox (2010); Jackman (2011); R Core Team (2011); R Development Core Team (2011); R Core Team (2012)

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3

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Introduction

4

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Principaux résultats

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5

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Principaux résultats

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ABC

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Chapitre 1

titre francais

—

titre anglais

blabla

McCullagh and Nelder (1989); Hastie and Tibshirani (1990); Grambsch and Therneau (1994); Fahrmeir (1994); Fahrmeir and Tutz (1994); Hastie and Tibshirani (1995); Johnson et al. (1997); Wood (2001); Kotz et al. (2002); Venables and Ripley (2002); Wood (2003);

Clark and Thayer (2004)

Bibliography

Breslow, N. (1974), ‘Covariance analysis of censored data’,Biometrics30, 89–99.

Clark, D. R. and Thayer, C. A. (2004), ‘A primer on the exponential family of distributions’, 2004 call paper program on generalized linear models.

Cleveland, W. S. (1979), ‘Robust locally weighted regression and smoothing scatterplots’, Journal of the American Statistical Association .

Cox, D. R. (1972), ‘Regression models and life-tables’,Journal of the Royal Statistical Society:

Series B.

Efron, B. (1977), ‘The efficiency of cox’s likelihood function for censored data’,Journal of the American Statistical Association72(359), 557–565.

Fahrmeir, L. (1994), ‘Dynamic modelling and penalized likelihood estimation for discrete time survival data’, Biometrika81(2), 317–330.

Fahrmeir, L. and Tutz, G. (1994), Multivariate Statistical Modelling Based on Generalized Linear Models, Springer.

Grambsch, P. and Therneau, T. (1994), ‘Proportional hazard tests and diagnostics based on weighted residuals’,Biometrika 81, 515–526.

Hastie, T. J. and Tibshirani, R. J. (1990),Generalized Additive Models, Chapman and Hall.

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Chapitre 1. titre court

Hastie, T. J. and Tibshirani, R. J. (1995), ‘Generalized additive models’, to appear in Ency- clopedia of Statistical Sciences.

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1997), Discrete Multivariate Distributions, Wiley Interscience.

Kalbfleisch, J. D. and Prentice, R. L. (1973), ‘Marginal likelihoods based on cox’s regression and life model’,Biometrika 60, 267–278.

Kaplan, E. L. and Meier, P. (1958), ‘Nonparametric estimation from incomplete observations’, Journal of the American Statistical Association 53(282), 457–481.

Kotz, S., Balakrishnan, N. and Johnson, N. L. (2002),Continuous Multivariate Distributions, Vol. 1, Wiley Interscience.

McCullagh, P. and Nelder, J. A. (1989),Generalized Linear Models, 2nd edn, Chapman and Hall.

Nelder, J. A. and Wedderburn, R. W. M. (1972), ‘Generalized linear models’,Journal of the Royal Statistical Society .

Venables, W. N. and Ripley, B. D. (2002),Modern Applied Statistics with S, 4th edn, Springer.

Wood, S. N. (2001), ‘mgcv: GAMs and Generalized Ridge Regression for R’,R News1, 20–25.

Wood, S. N. (2003), ‘Thin plate regression splines’, Journal of the Royal Statistical Society:

Series B65(1), 95–114.

Appendix

1.0.1 appendix blalbal

Kaplan and Meier (1958); Cox (1972); Nelder and Wedderburn (1972); Kalbfleisch and Prentice (1973); Breslow (1974); Efron (1977); Cleveland (1979)

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DEF

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Chapitre 2

titre francais

—

titre anglais

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Johnson et al. (2005); Tableman and Kim (2005); Faraway (2006); Martinussen and Scheike (2006); Steihaug (2007); Zeileis et al. (2008); Wood (2008); Aalen et al. (2008); Arnold (2008);

Turner (2008); Therneau and Lumley (2009); Wood (2010)

Bibliography

Aalen, O., Borgan, O. and Gjessing, H. (2008),Survival and Event History Analysis, Springer.

Arnold, B. C. (2008), Pareto distributions, in ‘Encyclopedia of Statistical Sciences’, Wiley Interscience.

Series B.

Faraway, J. J. (2006), Extending the Linear Model with R: Generalized Linear, Mixed Effects and Parametric Regression Models, CRC Taylor& Francis.

Johnson, N. L., Kotz, S. and Kemp, A. W. (2005),Univariate discrete distributions, 3rd edn, Wiley Interscience.

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Martinussen, T. and Scheike, T. H. (2006), Dynamic Regression models for survival data, Springer.

Steihaug, T. (2007), Splines and b-splines: an introduction, Technical report, University of Oslo.

Tableman, M. and Kim, J. S. (2005), Survival Analysis using S: Analysis of time-to-event data, Chapman and Hall.

Therneau, T. and Lumley, T. (2009),survival: Survival analysis, including penalised likelihood.

R package version 2.35-8.

URL:http://CRAN.R-project.org/package=survival

Turner, H. (2008), Introduction to generalized linear models, Technical report, Vienna Uni- versity of Economics and Business.

Wood, S. N. (2008), ‘Fast stable direct fitting and smoothness selection for generalized additive models’, Journal of the Royal Statistical Society: Series B70(3).

Wood, S. N. (2010), ‘Fast stable reml and ml estimation of semiparametric glms’,Journal of the Royal Statistical Society: Series B.

Zeileis, A., Kleiber, C. and Jackman, S. (2008), ‘Regression models for count data in r’,Journal of Statistical Software27(8).

Appendix

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Chapitre 3

titre francais

—

titre anglais

blabla

Bibliography

Series B.

Appendix

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16

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Conclusion

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Conclusion et perspectives

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Conclusion et perspectives

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BIBLIOGRAPHIE

Bibliographie

Aalen, O., Borgan, O. et Gjessing, H. (2008). Survival and Event History Analysis.

Springer.

Arnold, B. C. (2008). Pareto distributions. In Encyclopedia of Statistical Sciences. Wiley Interscience.

Breslow, N. (1974). Covariance analysis of censored data. Biometrics, 30:89–99.

Clark, D. R. etThayer, C. A. (2004). A primer on the exponential family of distributions.

2004 call paper program on generalized linear models.

Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots.

Journal of the American Statistical Association.

Cox, D. R. (1972). Regression models and life-tables.Journal of the Royal Statistical Society : Series B.

Efron, B. (1977). The efficiency of cox’s likelihood function for censored data. Journal of the American Statistical Association, 72(359):557–565.

Fahrmeir, L. (1994). Dynamic modelling and penalized likelihood estimation for discrete time survival data. Biometrika, 81(2):317–330.

Fahrmeir, L. et Tutz, G. (1994). Multivariate Statistical Modelling Based on Generalized Linear Models. Springer.

Faraway, J. J. (2006).Extending the Linear Model with R : Generalized Linear, Mixed Effects and Parametric Regression Models. CRC Taylor& Francis.

Fox, J. (2010). Logit and probit models. Rapport technique, York SPIDA.

Grambsch, P. etTherneau, T. (1994). Proportional hazard tests and diagnostics based on weighted residuals. Biometrika, 81:515–526.

Hastie, T. J. et Tibshirani, R. J. (1990). Generalized Additive Models. Chapman and Hall.

Hastie, T. J. etTibshirani, R. J. (1995). Generalized additive models. to appear in Ency- clopedia of Statistical Sciences.

Jackman, S. (2011). pscl : Classes and Methods for R Developed in the Political Science Computational Laboratory, Stanford University. Department of Political Science, Stanford University. R package version 1.04.1.

Johnson, N. L.,Kotz, S. etBalakrishnan, N. (1997). Discrete Multivariate Distributions.

Wiley Interscience.

Johnson, N. L.,Kotz, S. et Kemp, A. W. (2005). Univariate discrete distributions. Wiley Interscience, 3rd édition.

Kalbfleisch, J. D. etPrentice, R. L. (1973). Marginal likelihoods based on cox’s regression and life model. Biometrika, 60:267–278.

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Conclusion et perspectives

Kaplan, E. L. etMeier, P. (1958). Nonparametric estimation from incomplete observations.

Journal of the American Statistical Association, 53(282):457–481.

Kotz, S., Balakrishnan, N. etJohnson, N. L. (2002). Continuous Multivariate Distribu- tions, volume 1. Wiley Interscience.

Martinussen, T. et Scheike, T. H. (2006). Dynamic Regression models for survival data.

Springer.

McCullagh, P. et Nelder, J. A. (1989). Generalized Linear Models. Chapman and Hall, 2nd édition.

Nelder, J. A. et Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society.

R Core Team (2011). R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

R Core Team (2012). R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

R Development Core Team (2010). R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

R Development Core Team (2011). R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

Steihaug, T. (2007). Splines and b-splines : an introduction. Rapport technique, University of Oslo.

Tableman, M. et Kim, J. S. (2005). Survival Analysis using S : Analysis of time-to-event data. Chapman and Hall.

Therneau, T. et Lumley, T. (2009). survival : Survival analysis, including penalised likelihood. R package version 2.35-8.

Turner, H. (2008). Introduction to generalized linear models. Rapport technique, Vienna University of Economics and Business.

Venables, W. N. etRipley, B. D. (2002). Modern Applied Statistics with S. Springer, 4th édition.

Wood, S. N. (2001). mgcv : GAMs and Generalized Ridge Regression for R. R News, 1:20–25.

Wood, S. N. (2003). Thin plate regression splines. Journal of the Royal Statistical Society : Series B, 65(1):95–114.

Wood, S. N. (2008). Fast stable direct fitting and smoothness selection for generalized additive models. Journal of the Royal Statistical Society : Series B, 70(3).

Wood, S. N. (2010). Fast stable reml and ml estimation of semiparametric glms. Journal of the Royal Statistical Society : Series B.

Zeileis, A., Kleiber, C. et Jackman, S. (2008). Regression models for count data in r.

Journal of Statistical Software, 27(8).

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