In analytic studies, survey data are often obtained with complex sampling designs and the resulting analyses require special attention to handle the sampling design. When the distribution in the sample is different from the distribution in the population, the sampling design is called informative and the analytic inference under informative sampling becomes more complicated. In this talk, some recent topics on informative sampling are covered. Topics include optimal estimation, Bootstrap-based test, analysis of multilevel models, Bayesian inference, and multiple imputation under informative sampling.
The talk is sponsored by the CANSSI CRT project Statistical Inference for Complex Surveys with Missing Observations.