Bayesian statistics represents a powerful framework for data analysis that centres on Bayes’ theorem, enabling researchers to update existing beliefs with incoming evidence. By combining prior ...

It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue ...

The Annals of Applied Statistics, Vol. 8, No. 2 (June 2014), pp. 852-885 (34 pages) Poverty maps are used to aid important political decisions such as allocation of development funds by governments ...

Bayesian Dynamic Models have been studied extensively in the last years. Recently, Gamerman and Migon (1993) introduced Bayesian Dynamic Hierarchical Models, that can be applied to longitudinal data ...

What Is A Hierarchical Models? Hierarchical models, also known as hierarchical statistical models, multilevel models or random-effects models, are tools for analysing data with a nested or grouped ...

Mike Lee receives relevant research funding from the Australian Research Council, the Australia-Pacific Science Foundation, and Flinders University. Benedict King receives funding from the Australian ...