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

TITRE SUR PLUSIEURS LIGNES,SUR PLUSIEURS LIGNES

THESE

presentee et soutenue publiquement le Date

pour l’obtention du

Doctorat de l’Universite Claude Bernard Lyon I

(mathematiques appliquees)

par

truc Bidulle

Composition du jury

Rapporteurs : truc Bidulle, Professeur atruc Bidulle, Professeur a

Examinateurs : truc Bidulle, Professeur atruc Bidulle, Professeur atruc Bidulle, Professeur atruc Bidulle, Professeur a

Laboratoire Science Actuarielle Financiere — EA 2429

Remerciements

Je voudrais commencer par remercier

i

À truc

iii

Résumé

title

blalba

Mots-clés: bidulle, truc

Abstract

title

blalbal

Keywords: bidulle truc

Table des matières

Remerciements i

Résumé v

Tables des matières ix

Introduction générale

Introduction 3

blalba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

blalba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Principaux résultats 5

ABC

Chapitre 1 titre court 9

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

ix

Table des matières

DEF


Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14


Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Conclusion

Conclusion et perspectives 19

Bibliographie 21

x

Introduction générale

1

Introduction

blabla

blalba

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

Development Core Team (2011); R Core Team (2012)

blalba

blabla

3

Introduction

4

Principaux résultats

blabla

5

Principaux résultats

6

ABC

7

Chapitre 1

titre francais—

titre anglais

blablaMcCullagh and Nelder (1989); Hastie and Tibshirani (1990); Grambsch and Therneau

(1994); Fahrmeir (1994); Fahrmeir and Tutz (1994); Hastie and Tibshirani (1995); Johnsonet 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’, Biometrics 30, 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 theAmerican Statistical Association 72(359), 557–565.

Fahrmeir, L. (1994), ‘Dynamic modelling and penalized likelihood estimation for discrete timesurvival data’, Biometrika 81(2), 317–330.

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

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

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

9

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 regressionand 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 andHall.

Nelder, J. A. and Wedderburn, R. W. M. (1972), ‘Generalized linear models’, Journal of theRoyal 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 News 1, 20–25.

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

Appendix

1.0.1 appendix

blalbalKaplan and Meier (1958); Cox (1972); Nelder and Wedderburn (1972); Kalbfleisch and

Prentice (1973); Breslow (1974); Efron (1977); Cleveland (1979)

10

DEF

11

Chapitre 2

titre francais—

titre anglais

blalblaJohnson 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’, WileyInterscience.





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

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


13



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 ofOslo.

Tableman, M. and Kim, J. S. (2005), Survival Analysis using S: Analysis of time-to-eventdata, 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 additivemodels’, Journal of the Royal Statistical Society: Series B 70(3).

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

Zeileis, A., Kleiber, C. and Jackman, S. (2008), ‘Regression models for count data in r’, Journalof Statistical Software 27(8).

Appendix

2.0.2 appendix

blalbalKaplan and Meier (1958); Cox (1972); Nelder and Wedderburn (1972); Kalbfleisch and


14

Chapitre 3

titre francais—

titre anglais

blablaKaplan and Meier (1958); Cox (1972); Nelder and Wedderburn (1972); Kalbfleisch and


Bibliography








Appendix

3.0.3 appendix

blalbal

15


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

16

Conclusion

17

Conclusion et perspectives

blalblalb

19


20

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. WileyInterscience.

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

Clark, D. R. et 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 ofthe American Statistical Association, 72(359):557–565.

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

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

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

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

Grambsch, P. et Therneau, T. (1994). Proportional hazard tests and diagnostics based onweighted residuals. Biometrika, 81:515–526.

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

Hastie, T. J. et Tibshirani, 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 ScienceComputational Laboratory, Stanford University. Department of Political Science, StanfordUniversity. R package version 1.04.1.

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

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

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

21


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

Kotz, S., Balakrishnan, N. et Johnson, 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 ofthe Royal Statistical Society.

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

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

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

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

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

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

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

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

Venables, W. N. et Ripley, B. D. (2002). Modern Applied Statistics with S. Springer, 4thédition.

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 additivemodels. Journal of the Royal Statistical Society : Series B, 70(3).

Wood, S. N. (2010). Fast stable reml and ml estimation of semiparametric glms. Journal ofthe 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).

22

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