The Annals of Statistics
- Ann. Statist.
- Volume 15, Number 2 (1987), 670-693.
The Robustness and Sensitivity of the Mixed-Dirichlet Bayesian Test for "Independence" in Contingency Tables
A mixed-Dirichlet prior was previously used to model the hypotheses of "independence" and "dependence" in contingency tables, thus leading to a Bayesian test for independence. Each Dirichlet has a main hyperparameter $\kappa$ and the mixing is attained by assuming a hyperprior for $\kappa$. This hyperparameter can be regarded as a flattening or shrinking constant. We here review the method, generalize it and check the robustness and sensitivity with respect to variations in the hyperpriors and in their hyperhyperparameters. The hyperpriors examined included generalized log-Students with various numbers of degrees of freedom $\nu$. When $\nu$ is as large as 15 this hyperprior approximates a log-normal distribution and when $\nu = 1$ it is a log-Cauchy. Our experiments caused us to recommend the log-Cauchy hyperprior (or of course any distribution that closely approximates it). The user needs to judge values for the upper and lower quartiles, or any two quantiles, of $\kappa$, but we find that the outcome is robust with respect to fairly wide variations in these judgments.
Ann. Statist., Volume 15, Number 2 (1987), 670-693.
First available in Project Euclid: 12 April 2007
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Bayes factors against independence Bayesian robustness Bayesians (averaging over) Dirichlet-multinomial distribution flattening constants hierarchical Bayes hyper-hyperparameters log-Cauchy distribution log-normal distribution log-Student distribution mixtures of conjugate priors multinomial signficance tests multivaraite Bayesian methods shrinking constants
Good, I. J.; Crook, J. F. The Robustness and Sensitivity of the Mixed-Dirichlet Bayesian Test for "Independence" in Contingency Tables. Ann. Statist. 15 (1987), no. 2, 670--693. doi:10.1214/aos/1176350368. https://projecteuclid.org/euclid.aos/1176350368