Rahul Singh

Statistics and Probability · with Chernozhukov · 2018

A researcher in the Science corpus: 3 papers; trusts 51 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 101 citations into it; #4,582 of 12,932 by trust here, #7,508 by citations
• Artificial Intelligence — 23 citations into it
• Statistical Learning Theory — 18 citations into it; #585 of 1,038 by trust here, #863 by citations
• Causal Inference & Discovery — 15 citations into it; #145 of 364 by trust here, #295 by citations
• Computational Mechanics — 13 citations into it
• Management Science and Operations Research — 9 citations into it
• Economics and Econometrics — 8 citations into it

Whom Rahul Singh trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Victor Chernozhukov
2. Whitney K. Newey
3. Alexandre Belloni
4. James M. Robins
5. Adel Javanmard
6. Donald B. Rubin
7. Aad Van Der Vbart
8. Cun-Hui Zhang
9. Christian Hansen
10. Guido W. Imbens

Who trusts Rahul Singh overall

1. Ezequiel Smucler
2. Andrea Rotnitzky
3. Susan Athey
4. David A. Hirshberg
5. Stefan Wager
6. Victor Chernozhukov
7. James M. Robins
8. Dominik Rothenhausler
9. Guido W. Imbens

Papers from Rahul Singh's perspective

Ordered by how strongly the researchers Rahul Singh trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Shrinkage and Selection via the Lasso
2. On asymptotically optimal confidence regions and tests for high-dimensional models
3. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR
4. Double/debiased machine learning for treatment and structural parameters
5. Introduction to Nonparametric Estimation
6. Confidence intervals for low dimensional parameters in high dimensional linear models
7. The Dantzig selector: Statistical estimation when P is much larger than n
8. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
9. Targeted Learning: Causal Inference for Observational and Experimental Data
10. Supplementary Appendix for"Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"

Their papers

• Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018) — 113 citations
• Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks (2018) — 595 citations
• Learning L2 Continuous Regression Functionals via Regularized Riesz Representers (2018) — 29 citations