Checking date: 22/05/2022


Course: 2022/2023

Sampling Methods for Data Science
(19379)
Study: Master in Statistics for Data Science (345)
EPI


Coordinating teacher: MOLINA PERALTA, ISABEL

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Probability Statistical Inference Programming in R
Objectives
ACQUISITION of KNOWLEDGE about: 1) Introduction to sample designs. Inference under a design versus inference under a model. Traditional direct and indirect sample estimators. 2) Sampling models at the level of small areas. Synthetic Post-stratified Estimator and Composite Estimator. Fay-Herriot area-level model. Model at the level of individuals with nested errors. EBLUP predictors. 3) Method and EB predictors. Extensions for categorical data models.
Skills and learning outcomes
Description of contents: programme
Common topics to the subjects: 1) Sampling distributions. 2) Multivariate distributions. 3) Conditional distributions on the observed sample. Specific topics to each subject: 1) Introduction to sample designs. Inference under a design versus inference under a model. Traditional direct and indirect sample estimators. 2) Test models at the level of small areas. Synthetic Post-stratified Estimator and Composite Estimator. Fay-Herriot area-level model. Model at the level of individuals with nested errors. EBLUP predictors. 3) Method and EB predictors. Nested error model. Extensions for categorical data models.
Learning activities and methodology
Theoretical classes Practical classes Laboratory practices Tutorials Team work Individual students' work
Assessment System
  • % end-of-term-examination 100
  • % of continuous assessment (assigments, laboratory, practicals...) 0
Basic Bibliography
  • Rao, J.N.K. and Molina, I.. Small Area Estimation. Wiley. 2015
Additional Bibliography
  • Särndal, C.E., Swenson, B. and Wretman, J.H.. Model Assisted Survey Sampling. Springer-Verlag. 1992

The course syllabus may change due academic events or other reasons.