Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Hierarchical Bayes Analysis of Smoking and Lung Cancer Data

  • Published : 2002.04.01
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Abstract

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.

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