Yinchu Zhu

Statistics and Probability · 2 papers · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 63 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 58 citations into it; #4,020 of 12,932 by trust here, #7,508 by citations
• Artificial Intelligence — 16 citations into it; #12,065 of 35,345 by trust here, #25,809 by citations
• Computational Mechanics — 14 citations into it; #2,978 of 10,001 by trust here, #6,582 by citations
• Statistical Learning Theory — 9 citations into it
• Finance — 7 citations into it
• Economics and Econometrics — 6 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Yinchu Zhu trusts overall

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

1. Victor Chernozhukov
2. Alexandre Belloni
3. Adel Javanmard
4. Cun-Hui Zhang
5. E.J. Candes
6. Robert Tibshirani
7. Peter Buhlmann
8. Sara A. van de Geer
9. Jianqing Fan
10. James H. Stock

Who trusts Yinchu Zhu overall

1. Victor Chernozhukov
2. Whitney K. Newey
3. Susan Athey
4. Christian Hansen
5. James M. Robins
6. Pragya Sur
7. Guido W. Imbens
8. Jelena Bradic
9. Damian Kozbur
10. Stefan Wager

Papers from Yinchu Zhu's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
3. The Dantzig selector: Statistical estimation when P is much larger than n
4. On Model Selection Consistency of Lasso
5. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR
6. On asymptotically optimal confidence regions and tests for high-dimensional models
7. The Adaptive Lasso and Its Oracle Properties
8. Controlling the false discovery rate: a practical and powerful approach to multiple testing
9. High-dimensional graphs and variable selection with the Lasso
10. Confidence intervals and hypothesis testing for high-dimensional regression

Their papers

• Linear Hypothesis Testing in Dense High-Dimensional Linear Models (2016) — 88 citations
• Breaking the curse of dimensionality in regression (2017) — 19 citations