6 papers
Rescuing double robustness: safe estimation under complete misspecification
Lorenzo Testa, Francesca Chiaromonte, Kathryn Roeder
Double robustness is a major selling point of semiparametric and missing data methodology. Its virtues lie in protection against partial nuisance misspecification and asymptotic se…
Semiparametric semi-supervised learning for general targets under distribution shift and decaying overlap
Lorenzo Testa, Qi Xu, Jing Lei +1
In modern scientific applications, large volumes of covariate data are readily available, while outcome labels are costly, sparse, and often subject to distribution shift. This asy…
Sparse group principal component analysis via double thresholding with application to multi-cellular programs
Qi Xu, Jing Lei, Kathryn Roeder
Multi-cellular programs (MCPs) are coordinated patterns of gene expression across interacting cell types that collectively drive complex biological processes such as tissue develop…
Towards Efficient Inference under Nonmonotone Missingness with General Imputation
Qi Xu, Lorenzo Testa, Jing Lei +1
Missing data are ubiquitous in classical survey and longitudinal studies as well as modern multi-modality data analysis. A longstanding challenge arises under nonmonotone missingne…
Adaptive Projected Two-Sample Comparisons for Single-Cell Gene Expression Data
Tianyu Zhang, Jing Lei, Kathryn Roeder
We study high-dimensional two-sample mean comparison and address the curse of dimensionality through data-adaptive projections. Leveraging the low-dimensional and localized signal…
Augmented Doubly Robust Post-Imputation Inference for Proteomic Data
Haeun Moon, Jin-Hong Du, Jing Lei +1
Quantitative measurements produced by mass spectrometry proteomics experiments offer a direct way to explore the role of proteins in molecular mechanisms. However, analysis of such…