R´ ef´ erences
[1] J. Adler : R in a Nutshell. O’Reilly, 2010.
[2] H. Akaike : Information Theory and an Extension of the Maximum
Likelihood Principle. In Petrov B. N., Csaki F., ´ editeurs : Proc. of the
2nd Int. Symp. on Information Theory, pages 267-81, 1973.
[3] A. Antoniadis, J. Berruyer, R. Carmona : R´ egression non lin´ eaire
et applications. Economica, 1992.
[4] Y. Aragon : S´ eries temporelles avec R. M´ ethodes et cas. Springer, Collection Pratique R, 1
re ´ edition, 2011.
[5] J. M. Bardet, J. M. Azais : Le mod` ele lin´ eaire par l’exemple : R´ egression,
Analyse de la variance et Plans d’exp´ erience illustr´ es avec R, SAS et Splus.
Dunod, Sciences Sup, 2006.
[6] R. A. Becker, J. M. Chambers, A. R. Wilks : The New S Language:
A Programming Environment for Data Analysis and Graphics. Chapman
& Hall, 1988.
[7] D. A. Belsley, E. Kuh, R. E. Welsch : Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons,
New York-Chichester-Brisbane, 1980. Wiley Series in Probability and Mathematical Statistics.
[8] M. Bilodeau, P. Lafaye de Micheaux : A-dependence Statistics for
Mutual and Serial Independence of Categorical Variables. Journal of Statistical Planning and Inference, 139:2407-19, 2009.
[9] J. W. Braun, D. J. Murdoch : A First Course in Statistical Programming
with R. Cambridge University Press, 1st edition, January 2008.
[10] P. Burns : The R inferno. Unpublished manual, 2011.
[11] J. M. Chambers : Software for Data Analysis : Programming with R.
Statistics and Computing. Springer, June 2008.
[12] I. Chivers : Introduction to Programming with Fortran: With Coverage
of Fortran 90, 95, 2003, 2008 and 77. Springer, 2nd edition, 2012.
[13] Y. Cohen, J. Cohen : Statistics and Data with R: An Applied Approach
Through Examples. Wiley, 2008.
[1] J. Adler : R in a Nutshell. O’Reilly, 2010.
[2] H. Akaike : Information Theory and an Extension of the Maximum
Likelihood Principle. In Petrov B. N., Csaki F., ´ editeurs : Proc. of the
2nd Int. Symp. on Information Theory, pages 267-81, 1973.
[3] A. Antoniadis, J. Berruyer, R. Carmona : R´ egression non lin´ eaire
et applications. Economica, 1992.
[4] Y. Aragon : S´ eries temporelles avec R. M´ ethodes et cas. Springer, Collection Pratique R, 1
re ´ edition, 2011.
[5] J. M. Bardet, J. M. Azais : Le mod` ele lin´ eaire par l’exemple : R´ egression,
Analyse de la variance et Plans d’exp´ erience illustr´ es avec R, SAS et Splus.
Dunod, Sciences Sup, 2006.
[6] R. A. Becker, J. M. Chambers, A. R. Wilks : The New S Language:
A Programming Environment for Data Analysis and Graphics. Chapman
& Hall, 1988.
[7] D. A. Belsley, E. Kuh, R. E. Welsch : Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons,
New York-Chichester-Brisbane, 1980. Wiley Series in Probability and Mathematical Statistics.
[8] M. Bilodeau, P. Lafaye de Micheaux : A-dependence Statistics for
Mutual and Serial Independence of Categorical Variables. Journal of Statistical Planning and Inference, 139:2407-19, 2009.
[9] J. W. Braun, D. J. Murdoch : A First Course in Statistical Programming
with R. Cambridge University Press, 1st edition, January 2008.
[10] P. Burns : The R inferno. Unpublished manual, 2011.
[11] J. M. Chambers : Software for Data Analysis : Programming with R.
Statistics and Computing. Springer, June 2008.
[12] I. Chivers : Introduction to Programming with Fortran: With Coverage
of Fortran 90, 95, 2003, 2008 and 77. Springer, 2nd edition, 2012.
[13] Y. Cohen, J. Cohen : Statistics and Data with R: An Applied Approach
Through Examples. Wiley, 2008.