Non parametric statistics: smoothings methods
[STAT2150]
[15h + 5h] 4 credits
This course is taught in the 1st semester.
Teacher(s): Rainer von Sachs
Language: French
Level: Second
Aims
Main themes
The topics treated during this course are :
1. Nonparametric estimation of a distribution function
2. Nonparametric estimation of a density function : the kernel method
3. Nonparametric estimation of a regression function :
- kernel estimation
- local polynomial estimation
- spline estimation
The material will essentially be treated from an applied point of view of methodology. The student will study software applications of the proposed methods.
Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)
Basic knowledge about probability and statistics: descriptive statistics, calculating probabilities, distribution function, probability density, means, variances (conditionally or not), linear regression. It is advisable (but not necessary) to follow the course STAT2140 before.
References
Fan, J. and Gijbels, I. (1996). Local polynomial modelling and its applications. Chapman & Hall, New York.
Green, P.J. and Silverman, B.W. (2000). Nonparametric regression and generalized linear models. Chapman & Hall, New York.
Härdle, W. (1990): Applied Nonparametric Regression. Cambridge University Press, Cambridge.
Hart, J.D. (1997). Nonparametric smoothing and lack-of-fit tests. Springer, New York.
Loader, C. (1999). Local regression and likelihood. Springer, New York.
Silverman, B.W. (1986) : Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.
Simonoff, J.S. (1996). Smoothing methods in Statistics. Springer.