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Abhishek Chakrabortty
Abhishek Chakrabortty
Assistant Professor, Department of Statistics, Texas A&M University
Verified email at stat.tamu.edu - Homepage
Title
Cited by
Cited by
Year
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
1222022
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
842017
Efficient and adaptive linear regression in semi-supervised settings
A Chakrabortty, T Cai
The Annals of Statistics 46 (4), 1541-1572, 2018
782018
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
212018
Estimating average treatment effects with a double‐index propensity score
D Cheng, A Chakrabortty, AN Ananthakrishnan, T Cai
Biometrics 76 (3), 767-777, 2020
162020
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
132023
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
122019
Semi-supervised quantile estimation: Robust and efficient inference in high dimensional settings
A Chakrabortty, G Dai, RJ Carroll
arXiv preprint arXiv:2201.10208, 2022
102022
A general framework for treatment effect estimation in semi-supervised and high dimensional settings
A Chakrabortty, G Dai, ET Tchetgen
arXiv preprint arXiv:2201.00468, 2022
82022
Robust Semi-Parametric Inference in Semi-Supervised Settings
A Chakrabortty
Harvard University, 2016
62016
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
52020
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
32023
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
32017
Tail bounds for canonical U-statistics and U-processes with unbounded kernels
A Chakrabortty, AK Kuchibhotla
Working paper, Wharton School, University of Pennsylvania, 2018
22018
Supplement to “Efficient and adaptive linear regression in semi-supervised settings.”
A Chakrabortty, T Cai
DOI, 2018
12018
A Poisson regression model for association mapping of count phenotypes
S Ghosh, A Chakrabortty
Molecular Cytogenetics 7 (Suppl 1), O1, 2014
12014
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
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
2016
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