Estimation of Measures for Two-Way Contingency Tables Using the Bayesian Estimators

In the analysis of two-way contingency tables, the measures for representing the degree of departure from independence, symmetry or asymmetry are often used. These measures in contingency tables are expressed as functions of the probability structure of the tables. Hence, the value of a measure is estimated. Plug-in estimators of measures with sample proportions are used to estimate the measures, but without sufficient sample size, the bias and mean squared error (MSE) of the estimators become large. This study proposes an estimator that can reduce the bias and MSE, even without a sufficient sample size, using the Bayesian estimators of cell probabilities. We asymptotically evaluate the MSE of the estimator of the measure plugging in the posterior means of the cell probabilities when the prior distribution of the cell probabilities is the Dirichlet distribution. As a result, we can derive the Dirichlet parameter that asymptotically minimizes the MSE of the estimator. Numerical experiments show that the proposed estimator has a smaller bias and MSE than the plug-in estimator with sample proportions, uniform prior, and Jeffreys prior. Another advantage of our approach is the construction of credible intervals for measures using Monte Carlo simulations.