Donglin Zeng

Statistics and Probability · 2005–2018

A researcher in the Science corpus: 15 papers; trusts 87 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 168 citations into it; #842 of 12,932 by trust here, #823 by citations
• Artificial Intelligence — 41 citations into it; #12,271 of 35,345 by trust here, #19,439 by citations
• Statistical Learning Theory — 24 citations into it
• Causal Inference & Discovery — 23 citations into it
• Molecular Biology — 20 citations into it; #9,183 of 17,773 by trust here, #15,203 by citations
• Economics and Econometrics — 15 citations into it
• Genetics — 15 citations into it; #3,888 of 6,854 by trust here, #6,487 by citations
• Pharmacology — 13 citations into it; #133 of 1,112 by trust here, #415 by citations

Whom Donglin Zeng trusts overall

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

1. Susan A. Murphy
2. James M. Robins
3. Danyu Lin
4. Zhezhen Jin
5. Zhiliang Ying
6. Donald B. Rubin
7. Robin Henderson
8. Kani Chen
9. Bernard Walter Silverman
10. A. Gilat

Who trusts Donglin Zeng overall

1. Eric B. Laber
2. Michael R. Kosorok
3. Yi-Hau Chen
4. Torsten Hothorn
5. Yichi Zhang
6. Ying‐Qi Zhao
7. Kristin A. Linn
8. Xinkun Nie
9. Ying Liu
10. Yuanjia Wang

Papers from Donglin Zeng's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. Reinforcement Learning: An Introduction
3. ImageNet classification with deep convolutional neural networks
4. The Elements of Statistical Learning
5. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. The Nature of Statistical Learning Theory
8. Support-Vector Networks
9. Gradient-based learning applied to document recognition
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Estimating Individualized Treatment Rules Using Outcome Weighted Learning (2012) — 805 citations
• Maximum likelihood estimation in semiparametric regression models with censored data (2007) — 358 citations
• New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes (2015) — 263 citations
• Maximum Likelihood Estimation for the Proportional Odds Model With Random Effects (2005) — 65 citations
• Reinforcement Learning Strategies for Clinical Trials in Non-small Cell Lung Cancer (2011) — 219 citations
• Doubly Robust Learning for Estimating Individualized Treatment with Censored Data (2014) — 156 citations
• Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens (2018) — 95 citations
• Penalized Q-Learning for Dynamic Treatment Regimens (2015) — 101 citations
• Likelihood-Based Inference on Haplotype Effects in Genetic Association Studies (2006) — 152 citations
• Semiparametric Transformation Models With Random Effects for Recurrent Events (2007) — 105 citations
• Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens (2016) — 34 citations
• Efficient Estimation for the Accelerated Failure Time Model (2007) — 160 citations
• Learning Optimal Individualized Treatment Rules from Electronic Health Record Data (2016) — 25 citations
• Efficient estimation of semiparametric transformation models for counting processes (2006) — 222 citations
• Asymptotic results for maximum likelihood estimators in joint analysis of repeated measurements and survival time (2005) — 91 citations