The objective of the course is to present:
- the inference principles in finite population sampling theory,
- the most common sampling methods,
- the reweighting methods (calibration, or adjustment for non-response).
Main learning outcomes: at the end of the course, the student will know how to:
- Define a sampling design adapted to the problem posed, in the case of simple surveys,
- Calculate estimators and their precision,
- Correct a survey for unit or item non-response.
Written exam (100%).
- Finite population sampling. Sampling design, measures of accuracy, inclusion probabilities, Horvitz-Thompson estimation, choice of inclusion probabilities, confidence intervals, case of a function of totals.
- Simple random sampling. Estimating a mean, a total, a proportion. Calculation and estimation of precision. Determining the sample size. Sampling algorithms.
- Stratification. Principle. Estimation and estimation of precision. Sample allocation between strata. Constitution of strata.
- Unequal probability sampling. Sampling algorithms: Poisson sampling, systematic sampling, pivotal method, Chao's method.
- Multistage sampling. Cluster sampling: estimating a total, precision. Two-stage sampling: estimating a total, precision. Case of a simple random sample at each stage. Self-weighting sample surveys.
- Reweighting methods. Principles, calibration method and its applications: regression estimation, ratio estimation, post-stratification estimation.
- Correcting non-response. Type of non-response. Reweighting for unit non-response. Imputation for item non-response.
- Ardilly P. (2006). Les techniques de sondage. Technip, Paris.
- Sarndal C.E., Swenson B., Wretman J. (2003). Model assisted survey sampling. Springer, New-York.
- Tillé Y. (2020). Sampling and estimation from finite populations. Wiley, New-York.