89 citations · 455 across the 65 of their papers we have counts for
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Characterizing Identifiability in Boolean Factor Models
Mengqi Lin, Gongjun Xu
Boolean factor models, including prominent subfamilies such as Boolean matrix decompositions and cognitive diagnosis models, find broad applications ranging from social sciences to…
Marginal maximum likelihood estimation and asymptotic theory for latent variable models in high dimensions
Chengyu Cui, Gongjun Xu
This work addresses a longstanding gap in the statistical foundations of marginal maximum likelihood estimation for high-dimensional latent variable models. Marginal maximum likeli…
Consistency Theory of General Nonparametric Classification Methods in Cognitive Diagnosis
Chengyu Cui, Yanlong Liu, Gongjun Xu
Cognitive diagnosis models have been popularly used in fields such as education, psychology, and social sciences. While parametric likelihood estimation is a prevailing method for…
Statistical Inference on Latent Space Models for Network Data
Jinming Li, Shihao Wu, Chengyu Cui +2
Latent space models are powerful statistical tools for modeling and understanding network data. While the importance of accounting for uncertainty in network analysis has been well…
Statistical Inference for Noisy Incomplete Binary Matrix
Yunxiao Chen, Chengcheng Li, Jing Ouyang +1
We consider the statistical inference for noisy incomplete binary (or 1-bit) matrix. Despite the importance of uncertainty quantification to matrix completion, most of the categori…
On the Phase Transition of Wilk's Phenomenon
Yinqiu He, Bo Meng, Zhenghao Zeng +1
Wilk's theorem, which offers universal chi-squared approximations for likelihood ratio tests, is widely used in many scientific hypothesis testing problems. For modern datasets wit…
A Note on the Likelihood Ratio Test in High-Dimensional Exploratory Factor Analysis
Yinqiu He, Zi Wang, Gongjun Xu
The likelihood ratio test is widely used in exploratory factor analysis to assess the model fit and determine the number of latent factors. Despite its popularity and clear statist…
Likelihood Ratio Test in Multivariate Linear Regression: from Low to High Dimension
Yinqiu He, Tiefeng Jiang, Jiyang Wen +1
Multivariate linear regressions are widely used statistical tools in many applications to model the associations between multiple related responses and a set of predictors. To infe…
Sufficient and Necessary Conditions for the Identifiability of the -matrix
Yuqi Gu, Gongjun Xu
Restricted latent class models (RLCMs) have recently gained prominence in educational assessment, psychiatric evaluation, and medical diagnosis. Different from conventional latent…
Asymptotically Independent U-Statistics in High-Dimensional Testing
Yinqiu He, Gongjun Xu, Chong Wu +1
Many high-dimensional hypothesis tests aim to globally examine marginal or low-dimensional features of a high-dimensional joint distribution, such as testing of mean vectors, covar…
Partial Identifiability of Restricted Latent Class Models
Yuqi Gu, Gongjun Xu
Latent class models have wide applications in social and biological sciences. In many applications, pre-specified restrictions are imposed on the parameter space of latent class mo…
Sequential Analysis of Cox Model under Response Dependent Allocation
Xiaolong Luo, Gongjun Xu, Zhiliang Ying
Sellke and Siegmund (1983) developed the Brownian approximation to the Cox partial likelihood score as a process of calendar time, laying the foundation for group sequential analys…
Some Asymptotic Results of Gaussian Random Fields with Varying Mean Functions and the Associated Processes
Jingchen Liu, Gongjun Xu
In this paper, we derive tail approximations of integrals of exponential functions of Gaussian random fields with varying mean functions and approximations of the associated point…
Theory of self-learning -matrix
Jingchen Liu, Gongjun Xu, Zhiliang Ying
Cognitive assessment is a growing area in psychological and educational measurement, where tests are given to assess mastery/deficiency of attributes or skills. A key issue is the…