Bayesian Estimation for the Multivariate Hypergeometric Distribution Incorporating Information from Aggregated Observations

TL;DR

This paper develops Bayesian estimators for multivariate hypergeometric distributions that leverage aggregated data to improve estimation accuracy, demonstrating the benefits of incorporating side information.

Contribution

It introduces a Bayesian estimation method using symmetric multinomial priors that effectively integrates aggregated data for better parameter estimation.

Findings

Incorporating side information improves estimator accuracy.

Bayesian estimators outperform traditional methods.

Method applicable to various multivariate hypergeometric problems.

Abstract

In this short note, we consider the problem of estimating multivariate hypergeometric parameters under squared error loss when side information in aggregated data is available. We use the symmetric multinomial prior to obtain Bayes estimators. It is shown that by incorporating the side information, we can construct an improved estimator.