5 papers
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…
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…
A Generalized Additive Partial-Mastery Cognitive Diagnosis Model
Camilo Cárdenas-Hurtado, Sze Ming Lee, Yunxiao Chen +1
Cognitive diagnosis models (CDMs) are restricted latent class models widely used to measure attributes of interest in diagnostic assessments across education, psychology, biomedica…
When Composite Likelihood Meets Stochastic Approximation
Giuseppe Alfonzetti, Ruggero Bellio, Yunxiao Chen +1
A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inference when the li…
Unfolding the Network of Peer Grades: A Latent Variable Approach
Giuseppe Mignemi, Yunxiao Chen, Irini Moustaki
Peer grading is an educational system in which students assess each other's work. It is commonly applied under Massive Open Online Course (MOOC) and offline classroom settings. Wit…