Study Programmes 2016-2017
STAT0727-2  
Non-parametric statistics
Duration :
30h Th, 10h Pr, 20h Mon. WS
Number of credits :
Master in mathematics (120 ECTS)8
Lecturer :
Gentiane Haesbroeck
Language(s) of instruction :
French language
Organisation and examination :
Teaching in the first semester, review in January
Units courses prerequisite and corequisite :
Prerequisite or corequisite units are presented within each program
Learning unit contents :
The first part of the course is devoted to different basic nonparametric tests, in particular in the location case (test of central tendency for one or two populations).  The second part of the course is devoted to the bootstrap technique while the third is based on the nonparametric estimation of density functions, with a particular focus on kernel methods and the choice of the smoothing parameters. Depending on the evolution of the course, current developpements might be studied (depth functions...).
Learning outcomes of the learning unit :
At the end of the course, students will be able to use, in an appropriate way, nonparametric techniques in inference and density estimation.
Prerequisite knowledge and skills :
Inferential statistics (for example: probability and statistics III in the bachelor in mathematics)
Planned learning activities and teaching methods :
The course will be divided in ex-cathedra lectures for the theory and practicals for the exercises (ex-cathedra or in the computer room).
Mode of delivery (face-to-face ; distance-learning) :
The course is given face-to-face according to the official timetable given to students in the beginning of the year.
Recommended or required readings :
Some partial lecture notes are available. Moreover, when slides will be used during the class, they will be provided to the students beforehand.
Assessment methods and criteria :
The evaluation is divided into two parts:
 
- an exam orginased in January with a written part for the exercises and an oral part for the theory.
- homeworks that will be given throughout the first semester and that will be made with the software R.
 
 
Work placement(s) :
Organizational remarks :
Teaching language: French
Contacts :
Gentiane Haesbroeck, G. Haesbroeck@ulg.ac.be
 
Teaching Assistant: Marie Ernst, M.Ernst@ulg.ac.be