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stat.ME2026
Design-Conditional Prior Elicitation for Dirichlet Process Mixtures: A Unified Framework for Cluster Counts and Weight Control
JoonHo Lee
Dirichlet process mixture (DPM) models are widely used for semiparametric Bayesian analysis in educational and behavioral research, yet specifying the concentration parameter remai…
stat.ME2026
Reliability-Targeted Simulation of Item Response Data: Solving the Inverse Design Problem
JoonHo Lee
Monte Carlo simulations are the primary methodology for evaluating Item Response Theory (IRT) methods, yet marginal reliability - the fundamental metric of data informativeness - i…
stat.ME2024
Valid standard errors for Bayesian quantile regression with clustered and independent data
Feng Ji, JoonHo Lee, Sophia Rabe-Hesketh
In Bayesian quantile regression, the most commonly used likelihood is the asymmetric Laplace (AL) likelihood. The reason for this choice is not that it is a plausible data-generati…