Nonparametric statistics: smoothings methods

LSTAT2150  2018-2019  Louvain-la-Neuve

Nonparametric statistics: smoothings methods
4.0 credits
15.0 h + 5.0 h
1q

Teacher(s)
von Sachs Rainer ;
Language
Anglais
Main themes
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.
Aims

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”.

Other information
Prerequisites 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.
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Program title
Sigle
Credits
Prerequisites
Aims
Master [120] in data Science: Statistic
4
-

Master [120] in Economics: General
5
-

Master [120] in Mathematical Engineering
4
-

Master [120] in Data Science Engineering
4
-

Master [120] in data Science: Information technology
4
-

Master [120] in Statistic: Biostatistics
4
-

Master [120] in Statistic: General
4
-