6 papers · 1 filter
Inference with non-differentiable surrogate loss in a general high-dimensional classification framework
Muxuan Liang, Yang Ning, Maureen A Smith +1
Penalized empirical risk minimization with a surrogate loss function is often used to learn a high-dimensional linear decision rule in classification problems. Although much of the…
Targeted learning via probabilistic subpopulation matching
Xiaokang Liu, Jie Hu, Naimin Jing +4
In biomedical research, to obtain more accurate prediction results from a target study, leveraging information from multiple similar source studies is proved to be useful. However,…
Distributed inference for heterogeneous mixture models using multi-site data
Xiaokang Liu, Rui Duan, Raymond J. Carroll +2
Mixture models postulate the overall population as a mixture of finite subpopulations with unobserved membership. Fitting mixture models usually requires large sample sizes and com…
Incorporating External Controls for Estimating the Average Treatment Effect on the Treated with High-Dimensional Data: Retaining Double Robustness and Ensuring Double Safety
Chi-Shian Dai, Chao Ying, Yang Ning +1
Randomized controlled trials (RCTs) are widely regarded as the gold standard for causal inference in biomedical research. For instance, when estimating the average treatment effect…
G-HIVE: Parameter Estimation and Approximate Inference for Multivariate Response Generalized Linear Models with Hidden Variables
Inbeom Lee, Yang Ning
In practice, there often exist unobserved variables, also termed hidden variables, associated with both the response and covariates. Existing works in the literature mostly focus o…
Optimal Sampling for Generalized Linear Model under Measurement Constraint with Surrogate Variables
Yixin Shen, Yang Ning
Measurement-constrained datasets, often encountered in semi-supervised learning, arise when data labeling is costly, time-intensive, or hindered by confidentiality or ethical conce…