Incomplete data: semi-parametric and Bayesian methods (Winter Conference)
Incomplete data is a common phenomenon in longitudinal studies based on surveys and/or population registers. In such studies individuals are followed through time and data may be incomplete due, e.g., to drop out/attrition (individuals intentionally leave the study, individuals leave the study because they move or die, etc.) and censoring (due to end of study, death, etc). Incomplete data may also arise due to selection mechanisms, for instance, in meta-analyses (publication bias) and causal inference in observational studies. The course covers a wide range of incomplete data situations and discuss how they can be tackled, for instance, with semiparametric or Bayesian methods.
The course is based on the lectures given at the Winter Conference in Statistics in Hemavan, March 6-10 2011. GRAPES will refund the conference fee, housing and travel costs within Sweden for PhD students who participate in the course. Please see the conference home page for further information about the course content and registration information. Note that the registration dead line is January 17 2011.