Han Liu
- Statistics and Probability top 0.5%
- Statistical Methods and Inference 27
- Advanced Statistical Methods and Models 14
- Statistical Methods and Bayesian Inference 11
- Computational Mathematics top 10%
- Global and Planetary Change top 5%
- Land Use and Ecosystem Services 11
- Media Technology top 2%
- Artificial Intelligence top 2%
- Bayesian Methods and Mixture Models 9
- Bayesian Modeling and Causal Inference 5
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- Remote Sensing in Agriculture 7
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- Sparse and Compressive Sensing Techniques 4
- Journals
- The Annals of Statistics (5 papers)Remote Sensing (3 papers)Journal of the Royal Statistical Society Series B (Statistical Methodology) (3 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Han Liu
82 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Statistics and Probability 869
- Computational Mathematics 16
- Global and Planetary Change 516
- Media Technology 180
- Artificial Intelligence 508
Countries citing papers authored by Han Liu
This map shows the geographic impact of Han Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Han Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Han Liu more than expected).
Fields of papers citing papers by Han Liu
This network shows the impact of papers produced by Han Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Han Liu. The network helps show where Han Liu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Han Liu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 2 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 15 | |
| 4 | 2023 | 15 | |
| 5 | 2022 | 0 | |
| 6 | 2021 | 3 | |
| 7 | 2020 | 1 | |
| 8 | Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma | 2017 | 7 |
| 9 | 2017 | 5 | |
| 10 | 2016 | 83 | |
| 11 | 2016 | 22 | |
| 12 | 2016 | 44 | |
| 13 | SPARC: Optimal Estimation and Asymptotic Inference under Semiparametric Sparsity | 2014 | 1 |
| 14 | Optimal Rates of Convergence of Transelliptical Component Analysis | 2013 | 1 |
| 15 | Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model | 2013 | 2 |
| 16 | CODA: high dimensional copula discriminant analysis | 2013 | 34 |
| 17 | Transelliptical Graphical Models | 2012 | 28 |
| 18 | Semiparametric Principal Component Analysis | 2012 | 10 |
| 19 | Tree Density Estimation | 2010 | 4 |
| 20 | Multivariate Dyadic Regression Trees for Sparse Learning Problems | 2010 | 2 |
About Han Liu
Han Liu is a scholar working on Statistics and Probability, Computational Mathematics and Global and Planetary Change, having authored 88 papers that have together received 2.7k indexed citations. Recurring topics across this work include Statistical Methods and Inference (27 papers), Advanced Statistical Methods and Models (14 papers), Land Use and Ecosystem Services (11 papers), Statistical Methods and Bayesian Inference (11 papers), Bayesian Methods and Mixture Models (9 papers), Remote Sensing in Agriculture (7 papers), Bayesian Modeling and Causal Inference (5 papers) and Sparse and Compressive Sensing Techniques (4 papers). The work is most often cited by research in Statistics and Probability (869 citations), Computational Mathematics (16 citations) and Global and Planetary Change (516 citations). Han Liu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include John Lafferty, Larry Wasserman, Fang Han, Yang Ning, Peng Gong, Pradeep Ravikumar, Jie Wang, Ming Yuan, Nicholas Clinton and Shunlin Liang. Their work appears in journals such as The Annals of Statistics, Remote Sensing, Journal of the Royal Statistical Society Series B (Statistical Methodology), Earth system science data and Environmental Toxicology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.