Improving the accuracy of estimating indexes in contingency tables using Bayesian estimators

TL;DR

This paper introduces a Bayesian estimator for indexes in contingency tables that reduces bias and mean square error, especially with small sample sizes, outperforming traditional methods and priors.

Contribution

It proposes a novel Bayesian estimator for contingency table indexes using Dirichlet priors, improving estimation accuracy over existing methods.

Findings

The Bayesian estimator reduces bias and MSE with small samples.

It outperforms uniform and Jeffreys priors in estimation accuracy.

Numerical experiments confirm improved performance in practice.

Abstract

In contingency table analysis, one is interested in testing whether a model of interest (e.g., the independent or symmetry model) holds using goodness-of-fit tests. When the null hypothesis where the model is true is rejected, the interest turns to the degree to which the probability structure of the contingency table deviates from the model. Many indexes have been studied to measure the degree of the departure, such as the Yule coefficient and Cram\'er coefficient for the independence model, and Tomizawa's symmetry index for the symmetry model. The inference of these indexes is performed using sample proportions, which are estimates of cell probabilities, but it is well-known that the bias and mean square error (MSE) values become large without a sufficient number of samples. To address the problem, this study proposes a new estimator for indexes using Bayesian estimators of cell probabilities. Assuming the Dirichlet distribution for the prior of cell probabilities, we asymptotically evaluate the value of MSE when plugging the posterior means of cell probabilities into the index, and propose an estimator of the index using the Dirichlet hyperparameter that minimizes the value. Numerical experiments show that when the number of samples per cell is small, the proposed method has smaller values of bias and MSE than other methods of correcting estimation accuracy. We also show that the values of bias and MSE are smaller than those obtained by using the uniform and Jeffreys priors.