Yang Ning

27 catalog papers. Identity-linked publications and additional name matches are distinguished below.

ORCID: 0000-0001-6877-9231

Department of Statistics and Data Sciences, Cornell University , Ithaca, NY , USA

Publications

• Federated feature selection with false discovery rate control — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2026 (name-matched, not ORCID-linked)
• Inference with non-differentiable surrogate loss in a general high-dimensional classification framework — Journal of Machine Learning Research 2026 (name-matched, not ORCID-linked)
• Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data — Journal of Machine Learning Research 2025 (name-matched, not ORCID-linked)
• DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data — Journal of Machine Learning Research 2025 (name-matched, not ORCID-linked)
• Inference in High-Dimensional Multivariate Response Regression with Hidden Variables — Journal of the American Statistical Association 2024
• Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning — Journal of the American Statistical Association 2024
• Test of Significance for High-Dimensional Thresholds with Application to Individualized Minimal Clinically Important Difference — Journal of the American Statistical Association 2024
• Discussion of ‘Statistical inference for streamed longitudinal data’ — Biometrika 2023
• Adaptive estimation in multivariate response regression with hidden variables — The Annals of Statistics 2022
• Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score — Journal of Machine Learning Research 2022 (name-matched, not ORCID-linked)
• Heterogeneity-aware and communication-efficient distributed statistical inference — Biometrika 2022
• Nonregular and minimax estimation of individualized thresholds in high dimension with binary responses — The Annals of Statistics 2022
• High-Dimensional Inference for Cluster-Based Graphical Models — Journal of Machine Learning Research 2020 (name-matched, not ORCID-linked)
• On specification tests for composite likelihood inference — Biometrika 2020
• Adaptive estimation in structured factor models with applications to overlapping clustering — The Annals of Statistics 2020
• Robust estimation of causal effects via a high-dimensional covariate balancing propensity score — Biometrika 2020
• Test of significance for high-dimensional longitudinal data — The Annals of Statistics 2020
• Efficient augmentation and relaxation learning for individualized treatment rules using observational data — Journal of Machine Learning Research 2019 (name-matched, not ORCID-linked)
• On Semiparametric Exponential Family Graphical Models — Journal of Machine Learning Research 2018 (name-matched, not ORCID-linked)
• A conditional composite likelihood ratio test with boundary constraints — Biometrika 2018
• PLEMT: A Novel Pseudolikelihood-Based EM Test for Homogeneity in Generalized Exponential Tilt Mixture Models — Journal of the American Statistical Association 2017
• Testing and confidence intervals for high dimensional proportional hazards models — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2017
• High dimensional semiparametric latent graphical model for mixed data — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2017
• A likelihood ratio framework for high-dimensional semiparametric regression — The Annals of Statistics 2017
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models — The Annals of Statistics 2017
• Replicates in high dimensions, with applications to latent variable graphical models — Biometrika 2016
• High-dimensional semiparametric bigraphical models — Biometrika 2013 (name-matched, not ORCID-linked)