Topics to be treated
- General framework of inference in finite population; population, sampling, statistics for the inference based on experimental data, linear homogenous estimation: elementary units, complex units.
- Sampling with unequal probabilities: Hansen-Hurwitz and Horvitz-Thompson estimators, for the particular case of simple random sampling.
- Estimators improvement through auxiliary information: ratio estimator, regression estimator
- Sampling from complex units: stratified sampling, cluster sampling, two stages sampling.
- Sampling from biological populations: basic issues in sampling, estimation of the population size.
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”.
At the end of this learning unit, the student is able to :
Objective (in terms of abilities and knowledge) This course aims at providing the student the basic knowledges on the sampling methods, with a particular, but not exclusive, emphasis on sampling from (finite) human populations. At the end of the course, the student should be able to correctly designing a simple survey and analysing the results.
General framework of inference in finite population :
- Techniques of random samplings and estimators properties.
- Simple random sampling
- Stratified random sampling
- Uneven probability sampling
- Cluster sampling
- Multi-level sampling
Estimation improvement by use of auxiliary information.
8 x 2 hours of masterful presentations and 2 x 2 hours of practical exercices on computer.
Written examination in session : 14 points on 20.
Individual project delivered for the beginning of the first session : 6 points on 20.
MOODLEUCL : lecture LSTAT2200.
Tillé, Y. (2001). Théorie des sondages : échantillonnage et estimation en populations finies, (Cours et exercices avec solutions), Dunod, Paris.
Mouchart M. et J.-M. Rolin (1981), Enquêtes et Sondages, Série " Recyclage en Statistique ", Vol.5, , Louvain : U.C.L., Comité de Statistique.
Sharon Lohr (1999), Sampling : Design and Analysis, Duxbury Press Rao Poduri S.R.S. (2000), Sampling Methodologies with Applications, London : Chapman and Hall.
Title of the programme
Master  in Data Science Engineering
Master  in Economics: General
Master  in Statistic: General
Master  in data Science: Statistic
Master  in data Science: Information technology
Minor in Statistics and data sciences