Docente
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CONTI PIER LUIGI
(programma)
- Basic aspects on variable probability sampling designs
- Inclusion probabilities: properties and approximations
- Some relevant examples: simple random sampling, stratified random sampling, single-stage cluster sampling, two-stage sampling, systematic sampling, ppswr, ppswor, Midzuno-Lahiri sampling design
- Theory of statistical inference in random sampling under fixed-population approach: "flat" likelihood, minimal sufficient statistics, Rao-Blackwell theorem, non-completeness of the minimal sufficient statistic
- Efficiency and admissibility: non-existence of UMVUE
- Shrinkage estimators
- Horvitz-Thompson estimator and its properties. The problem of variance estimation: exact and approximate solutions.
- Calibration estimators, with applications to post-stratification and estimation in contingency tables. IPF algorithm.
- Sampling designs with pre-fixed inclusion probabilities: Poisson, Bernoulli, Pareto, Sampford, Conditional Poisson, Madow sampling designs.
- Balanced sampling designs and cube sampling algorithm
- Non-sampling errors: general aspects
- Frame errors
- Measurement errors models. Effects of measurement errors.
- Non-responses: general aspects. Methodologies to prevent non-responses. Methodologies to data weight.
- Nonrespondents sampling: the Hansen-Hurwitz approach in a modern perspective.
- Randomized response techniques
- Estimation of response probabilities via homogeneous response groups
- Superpopulation models: basic aspects
- Design-based, model-based, model-assisted approaches to inference for superpopulation parameters
- Ignorability of sampling designs and consequences of non-ignorability: the emerging of model-assisted inference.
- Weighted log-likelihood
- Regression models and GREG estimator
Lectures notes
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