6 papers
Statistical analysis of block structured latent variable models
Chengyu Cui, Gongjun Xu
This paper studies block structured latent variable models, in which observed variables are grouped into distinct blocks based on their relationships with the underlying latent var…
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…
Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation
Chengyu Cui, Gongjun Xu
Nonconvex methods have emerged as a dominant approach for low-rank matrix estimation, a problem that arises widely in machine learning and AI for learning and representing high-dim…
Accounting for Measurement Bias: A New Framework for Reliable Country Ranking in Large-Scale Educational Assessments
Jing Ouyang, Yunxiao Chen, Chengcheng Li +1
International Large-scale Assessments (ILSAs), such as the Program for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS)…
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…
Quantile Mediation Analytics
Canyi Chen, Yinqiu He, Huixia J. Wang +2
Mediation analytics help examine if and how an intermediate variable mediates the influence of an exposure variable on an outcome of interest. Quantiles, rather than the mean, of a…