von Sachs Rainer ;
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.
Second course of general education in nonparametric statistics, which mainly focuses on smoothing methods.
The contribution of this Teaching Unit to the development and
command of the skills and learning outcomes of the programme(s) can be
accessed at the end of this sheet, in the section entitled
“Programmes/courses offering this Teaching Unit”.
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.
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.
Master  in data Science: Statistic
Master  in Economics: General
Master  in Mathematical Engineering
Master  in Data Science Engineering
Master  in data Science: Information technology
Master  in Statistic: Biostatistics
Master  in Statistic: General