| Moving beyond sub-Gaussianity in high-dimensional statistics: Applications in covariance estimation and linear regression AK Kuchibhotla, A Chakrabortty Information and Inference: A Journal of the IMA 11 (4), 1389-1456, 2022 | 151 | 2022 |
| Efficient and adaptive linear regression in semi-supervised settings A Chakrabortty, T Cai The Annals of Statistics 46 (4), 1541-1572, 2018 | 97 | 2018 |
| Surrogate-assisted feature extraction for high-throughput phenotyping S Yu, A Chakrabortty, KP Liao, T Cai, AN Ananthakrishnan, VS Gainer, ... Journal of the American Medical Informatics Association 24 (e1), e143-e149, 2017 | 88 | 2017 |
| Inference for individual mediation effects and interventional effects in sparse high-dimensional causal graphical models A Chakrabortty, P Nandy, H Li arXiv preprint arXiv:1809.10652, 2018 | 23 | 2018 |
| Double robust semi-supervised inference for the mean: selection bias under MAR labeling with decaying overlap Y Zhang, A Chakrabortty, J Bradic Information and Inference: A Journal of the IMA 12 (3), 2066-2159, 2023 | 22 | 2023 |
| Semi-supervised quantile estimation: Robust and efficient inference in high dimensional settings A Chakrabortty, G Dai, RJ Carroll arXiv preprint arXiv:2201.10208, 2022 | 17 | 2022 |
| Estimating average treatment effects with a double‐index propensity score D Cheng, A Chakrabortty, AN Ananthakrishnan, T Cai Biometrics 76 (3), 767-777, 2020 | 17 | 2020 |
| High dimensional m-estimation with missing outcomes: A semi-parametric framework A Chakrabortty, J Lu, TT Cai, H Li arXiv preprint arXiv:1911.11345, 2019 | 17 | 2019 |
| A general framework for treatment effect estimation in semi-supervised and high dimensional settings A Chakrabortty, G Dai arXiv preprint arXiv:2201.00468, 2022 | 14 | 2022 |
| Semi-supervised causal inference: Generalizable and double robust inference for average treatment effects under selection bias with decaying overlap Y Zhang, A Chakrabortty, J Bradic arXiv preprint arXiv:2305.12789, 2023 | 8 | 2023 |
| Robust Semi-Parametric Inference in Semi-Supervised Settings A Chakrabortty Harvard University, 2016 | 8 | 2016 |
| Semi-supervised estimation of covariance with application to phenome-wide association studies with electronic medical records data SF Chan, BP Hejblum, A Chakrabortty, T Cai Statistical Methods in Medical Research 29 (2), 455-465, 2020 | 6 | 2020 |
| Tail bounds for canonical U-statistics and U-processes with unbounded kernels A Chakrabortty, AK Kuchibhotla Working paper, Wharton School, University of Pennsylvania, 2018 | 4 | 2018 |
| Surrogate aided unsupervised recovery of sparse signals in single index models for binary outcomes A Chakrabortty, M Neykov, R Carroll, T Cai arXiv preprint arXiv:1701.05230, 2017 | 4 | 2017 |
| Supplement to “Efficient and adaptive linear regression in semi-supervised settings.” A Chakrabortty, T Cai DOI, 2018 | 1 | 2018 |
| Improving Predictive Value of Gout Case Definitions in Electric Medical Records Utilizing Natural Language Processing: a Novel Informatics Approach SY Lim, SR Schoenfeld, A Chakrabortty, T Cai, A Cagan, V Gainer, ... ARTHRITIS & RHEUMATOLOGY 68, 2016 | 1 | 2016 |
| A Poisson regression model for association mapping of count phenotypes S Ghosh, A Chakrabortty Molecular Cytogenetics 7 (Suppl 1), O1, 2014 | 1 | 2014 |
| The Decaying Missing-at-Random Framework: Doubly Robust Causal Inference with Partially Labeled Data Y Zhang, A Chakrabortty, J Bradic arXiv preprint arXiv:2305.12789, 2023 | | 2023 |
| A GENERAL FRAMEWORK FOR TREATMENT EFFECT ESTIMATION IN SEMI-SUPERVISED AND HIGH DIMENSIONAL SETTINGS BY ABHISHEK CHAKRABORTTY, GUORONG DAI 2 AND ERIC TCHETGEN TCHETGEN 3 A CHAKRABORTTY, G DAI arXiv preprint arXiv:2201.00468, 2022 | | 2022 |
Arun Kumar KuchibhotlaCarnegie Mellon UniversityVerified email at cmu.edu
