Compound Estimation for Binomials
Published:Dec 31, 2025 18:38
•1 min read
•ArXiv
Analysis
This paper addresses the problem of estimating the mean of multiple binomial outcomes, a common challenge in various applications. It proposes a novel approach using a compound decision framework and approximate Stein's Unbiased Risk Estimator (SURE) to improve accuracy, especially when dealing with small sample sizes or mean parameters. The key contribution is working directly with binomials without Gaussian approximations, enabling better performance in scenarios where existing methods struggle. The paper's focus on practical applications and demonstration with real-world datasets makes it relevant.
Key Takeaways
- •Addresses the problem of estimating means of multiple binomial outcomes.
- •Proposes a compound decision framework and SURE for improved accuracy.
- •Works directly with binomials, avoiding Gaussian approximations.
- •Demonstrates the approach with real-world datasets.
Reference
“The paper develops an approximate Stein's Unbiased Risk Estimator (SURE) for the average mean squared error and establishes asymptotic optimality and regret bounds for a class of machine learning-assisted linear shrinkage estimators.”