2019/11/16 by Jaewoo Park, Ick Hoon Jin, Park, Jaewoo +3 · 1 citation
Computer Science · Mathematics · #Applications (stat.AP) #Bayesian Modeling and Causal Inference #Computation (stat.CO) #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #stat.AP #stat.CO
paper · pdf · doi:10.48550/arxiv.1911.07142
openalex publication_date 2019/11/16 · arxiv created 2021/05/19 · arxiv updated 2021/05/20 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Doubly-intractable posterior distributions arise in many applications of statistics concerned with discrete and dependent data, including physics, spatial statistics, machine learning, the social sciences, and other fields. A specific example is psychometrics, which has adapted high-dimensional Ising models from machine learning, with a view to studying the interactions among binary item responses in educational assessments. To estimate high-dimensional Ising models from educational assessment data, ℓ1-penalized nodewise logistic regressions have been used. Theoretical results in high-dimensional statistics show that ℓ1-penalized nodewise logistic regressions can recover the true interaction structure with high probability, provided that certain assumptions are satisfied. Those assumptions are hard to verify in practice and may be violated, and quantifying the uncertainty about the estimated interaction structure and parameter estimators is challenging. We propose a Bayesian approach that helps quantify the uncertainty about the interaction structure and parameters without requiring strong assumptions, and can be applied to Ising models with thousands of parameters. We demonstrate the advantages of the proposed Bayesian approach compared with ℓ1-penalized nodewise logistic regressions by simulation studies and applications to small and large educational data sets with up to 2,485 parameters. Among other things, the simulation studies suggest that the Bayesian approach is more robust against model misspecification due to omitted covariates than ℓ1-penalized nodewise logistic regressions.