3 papers
stat.ME2026
Robust Standard Errors for Bayesian Posterior Functionals via the Infinitesimal Jackknife
Nanyu Luo, Feng Ji
Quantitative research in the social and behavioral sciences relies heavily on nonlinear posterior functionals such as indirect effects, standardized coefficients, effect sizes, int…
cs.LG2026
Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimation
Biying Zhou, Nanyu Luo, Feng Ji
Item Response Theory (IRT) models are widely used to estimate respondents' latent abilities and calibrate item difficulty. Traditional IRT estimation typically requires centralizin…
stat.ML2025
Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm
Nanyu Luo, Feng Ji
Advances in deep learning and representation learning have transformed item factor analysis (IFA) in the item response theory (IRT) literature by enabling more efficient and accura…