Duration
30h Th, 10h Pr, 20h Mon. WS
Number of credits
| Master in mathematics (120 ECTS) (Even years, not organized in 2021-2022) | 8 crédits | |||
| Master in mathematics (60 ECTS) (Even years, not organized in 2021-2022) | 8 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course develops the following themes:
- Distribution function;
- Ranks and quantiles;
- Non parametric univariate tests;
- Non parametric estimation of density functions;
- Non parametric and quantile regression;
- Introduction to functional data analysis.
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, regression and density estimation. They will be prepared to set up and interpret regression models in finte or infite dimensional setting.
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, hybrid learning)
The course is taught only every other year, on "even" years.
Recommended or required readings
Some partial lecture notes are available. Moreover, the slides used during the class will be available on eCampus.
Some refereces:
- D. Bosq (1996). Nonparametric statistics for stochastic processes. Springer-Verlag.
- E.L. Lehmann (1999). Elements of large sample theory. Springer Texts in Statistics. Springer-Verlag.
- A. B. Tsybakov (2004). Introduction à l'estimation non-paramétrique. Springer-Verlag, Berlin, 2004.
- L. Wasserman (2006). All of nonparametric statistics. Springer Texts in Statistics. Springer-Verlag.
Assessment methods and criteria
The evaluation is divided into two parts:
- If face to face exam is possible: an exam organised in January, supplemented by a project.
- If distance exam is required: an exam organised in January, supplemented by a project.
Work placement(s)
Organizational remarks
Teaching language: French
Contacts
Amir Aboubacar, a.aboubacar@ulg.ac.be