Complex survey data provide rich information about socioeconomic phenomena. Analytic inferences can provide insight into the relationships between a response and a covariate. Small area estimates offer a granular understanding of a quantity of interest. Complex survey designs are often informative, meaning the variable of interest is correlated with the inclusion probability, after conditioning on the model covariates. Informative sample designs generate numerous challenges for the analysis. Unique approaches to inference under informative sampling are proposed in this session.

Model-based analyses of survey data are crucial for analytic inference and small area estimation. When the selection probability and the response variable are dependent, after conditioning on model covariates, the design is informative for the model. Ignoring an informative design can render invalid inferences. This session offers innovative methods for small area estimation and analytic inference in the presence of an informative design.

Organiser: Emily Berg

Chair: Emily Berg

Speaker: Yanghyeon Cho

Speaker: Dr Kosuke Morikawa

Speaker: Zhengyuan Zhu

Speaker: Abdulhakeem Eideh