Bayesian methods have become increasingly popular across a broad range of statistical applications, stimulated by the development of efficient computational methods such as MCMC. Sample survey inference, however, is still largely based on the randomization distribution over repeated sampling, with models being used only as a last resort, such as in small area estimation or when compensating for measurement errors or nonresponse.
The aim of this meeting is to highlight the potential advantages of Bayesian methodology and discuss and illustrate its possible applications in diverse areas of sample survey design and inference. The meeting will begin with a 1.5 days short course, followed by a 2.5 days conference, consisting of invited and contributed research and applied papers and a special panel discussion.
Further instructions about the conference and registration for the short course (PDF) is also available.
- Danny Pfeffermann, University of Southampton, UK & Hebrew University of Jerusalem, Israel
- Jon Forster, University of Southampton, UK
- Jon Rao, Carleton University, Canada
- Malay Ghosh, University of Florida, USA
- Rod Little, University of Michigan, USA
- Pedro Silva, University of Southampton, UK
- Pete Brodie, Office for National Statistics, UK