40 citations · 129 across the 14 of their papers we have counts for
12 papers · 1 filter
Bayesian Variable Selection for High-Dimensional Predictors with Missing Psychometric Outcomes
Zongyue Teng, Shujie Ma, Timothy J. Hohman +2
High-dimensional, multimodal predictors and partially observed multivariate outcomes are common in psychometric research. However, existing regularization methods often do not acco…
Sensitivity Analysis for Causal Decomposition Analysis: Assessing Robustness Toward Omitted Variable Bias
Soojin Park, Suyeon Kang, Chioun Lee +1
A key objective of decomposition analysis is to identify a factor (the 'mediator') contributing to disparities in an outcome between social groups. In decomposition analysis, a sch…
Nearly Optimal Learning using Sparse Deep ReLU Networks in Regularized Empirical Risk Minimization with Lipschitz Loss
Ke Huang, Mingming Liu, Shujie Ma
We propose a sparse deep ReLU network (SDRN) estimator of the regression function obtained from regularized empirical risk minimization with a Lipschitz loss function. Our framewor…
A fusion learning method to subgroup analysis of Alzheimer's disease
Mingming Liu, Jing Yang, Yushi Liu +4
Uncovering the heterogeneity in the disease progression of Alzheimer's is a key factor to disease understanding and treatment development, so that interventions can be tailored to…
Causal Inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders
Xiaohong Chen, Ying Liu, Shujie Ma +1
Semiparametric efficient estimation of various multi-valued causal effects, including quantile treatment effects, is important in economic, biomedical, and other social sciences. U…
Multivariate Functional Regression via Nested Reduced-Rank Regularization
Xiaokang Liu, Shujie Ma, Kun Chen
We propose a nested reduced-rank regression (NRRR) approach in fitting regression model with multivariate functional responses and predictors, to achieve tailored dimension reducti…