Hierarchical Bayes Analysis of Smoking and Lung Cancer Data Oh, Man-Suk; Park, Hyun-Jin;
Hierarchical models are widely used for inference on correlated parameters as a compromise between underfitting and overfilling problems. In this paper, we take a Bayesian approach to analyzing hierarchical models and suggest a Markov chain Monte Carlo methods to get around computational difficulties in Bayesian analysis of the hierarchical models. We apply the method to a real data on smoking and lung cancer which are collected from cities in China.
Hierarchical model;Correlated parameters;Markov chain Monte Carlo;Meta analysis;