Author(s)

Gary C. McDonald

Department of Mathematics and Statistics, Oakland University, Rochester, USA.

The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.

Bayesian Improved Surname and Geocoding (BISG), Mixture Likelihood Function, Posterior Distribution, Metropolis-Hastings Algorithms, Random Walk Chain, Independence Chain, Gibbs Sampling, WinBUGS

McDonald, G. (2024) A Bayesian Mixture Model Approach to Disparity Testing. Applied Mathematics, 15, 214-234. doi: 10.4236/am.2024.153012.