Bayesian methods (2)

Chair: Enrico Fabrizi

This session contains three contributions of Bayesian methods to small area estimation. The talks give methodological developments and applications related to small area models with misclassified covariates, non ignorable missing values in nested error regression models and Beta regression models for small area estimation of proportions.

Small area models with misclassified covariates

Modern small area estimation methods focus on mixed effects regression models that link the small areas and borrow strength from similar domains. However, when the auxiliary variables used in the models are measured with error, small area estimators that ignore such error may be worse than direct estimators ([1], [2]). In regression models, the presence […]

Nested Error Regression Models with Missing Values and Non-ignorable Non-response.

In most small area estimation problems there is a small sample size in each small area or segment. When the data have a two-level hierarchical structure, the nested error regression model proposed by Battese et al (1998) is a powerful tool for building a stable predictor. This model assumes that the sample is drawn at […]

Beta regression models for small area estimation of proportions

Linear mixed effects models have been popular in small area estimation problems for modeling survey data when the sample size in one or more areas is too small for reliable inference. However, when the data are restricted to a bounded interval, the linear model may be inappropriate, particularly if the data are near the boundary. […]

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