Daniel L. Chen
Statistics and Probability · with Belloni · 2012
A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 35 citations into it; #4,312 of 12,932 by trust here, #3,330 by citations
- Economics and Econometrics — 20 citations into it
- Artificial Intelligence — 12 citations into it
- Finance — 11 citations into it
- Computational Mechanics — 9 citations into it
- Management Science and Operations Research — 7 citations into it
Whom Daniel L. Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alexandre Belloni
- Robert Tibshirani
- Victor Chernozhukov
- Sara A. van de Geer
- Gary Chamberlain
- Christian Hansen
- Jinyong Hahn
- Jushan Bai
- Peter Buhlmann
- Serena Ng
Who trusts Daniel L. Chen overall
- Victor Chernozhukov
- Alexandre Belloni
- Christian Hansen
- Yang Ning
- D. Chetverikov
- Susan Athey
- Mehmet Caner
- A. Ahrens
- Xiaoying Tian
- Emily Leslie
Papers from Daniel L. Chen's perspective
Ordered by how strongly the researchers Daniel L. Chen trusts endorse each paper. Same corpus, a different order for every researcher.
- The Dantzig selector: Statistical estimation when P is much larger than n
- Regression Shrinkage and Selection via the Lasso
- SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR
- HIGH-DIMENSIONAL GENERALIZED LINEAR MODELS AND THE LASSO
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Supplementary Appendix for"Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
- Boosting for high-dimensional linear models
- Optimal Two‐Sided Invariant Similar Tests for Instrumental Variables Regression
- LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA
- Estimation of the Parameters of a Single Equation in a Complete System of Stochastic Equations
Their papers
- Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain (2012)
- Massachusetts Institute of Technology Department of Economics Working Paper Series Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain