Probability Statistical Inference Programming in R

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.

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.

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