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stat.ME2026

Endogeneity-Aware Cognitive Diagnostic Model for Multidomain Ordinal Assessments

Zhiyu Huang, Jing Ouyang, Kai Kang

Multidomain assessment batteries generate ordinal item responses that are often summarized through latent attribute profiles. Conventional cognitive diagnostic models (CDMs) provid…

stat.ME2026

Inference on Generalized Latent Variable Models with High-Dimensional Responses and Covariates

Jing Ouyang, Chengyu Cui, Yunxiao Chen +2

Regression models with both high-dimensional responses and covariates have attracted growing attention. Standard multivariate regression models become inadequate when the response…

stat.ME2026

Beyond Vintage Rotation: Bias-Free Sparse Representation Learning with Oracle Inference

Chengyu Cui, Yunxiao Chen, Jing Ouyang +1

Learning low-dimensional latent representations is a central topic in statistics and machine learning, and rotation methods have long been used to obtain sparse and interpretable r…

stat.ME2024

Statistical Inference for Covariate-Adjusted and Interpretable Generalized Factor Model with Application to Testing Fairness

Jing Ouyang, Chengyu Cui, Kean Ming Tan +1

Latent variable models are popularly used to measure latent factors (e.g., abilities and personalities) from large-scale assessment data. Beyond understanding these latent factors,…

stat.ME2023

A Note on Improving Variational Estimation for Multidimensional Item Response Theory

Chenchen Ma, Jing Ouyang, Chun Wang +1

Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimens…