Fang Han

3.4k total citations · 1 hit paper
50 papers, 1.7k citations indexed

About

Fang Han is a scholar working on Statistics and Probability, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Fang Han has authored 50 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Statistics and Probability, 20 papers in Artificial Intelligence and 6 papers in Signal Processing. Recurrent topics in Fang Han's work include Statistical Methods and Inference (30 papers), Advanced Statistical Methods and Models (19 papers) and Bayesian Methods and Mixture Models (13 papers). Fang Han is often cited by papers focused on Statistical Methods and Inference (30 papers), Advanced Statistical Methods and Models (19 papers) and Bayesian Methods and Mixture Models (13 papers). Fang Han collaborates with scholars based in United States, China and Germany. Fang Han's co-authors include Han Liu, Jianqing Fan, Han Liu, John Lafferty, Ming Yuan, Larry Wasserman, Han Liu, Mathias Drton, Cun‐Hui Zhang and Shizhe Chen and has published in prestigious journals such as Journal of the American Statistical Association, IEEE Transactions on Pattern Analysis and Machine Intelligence and Biometrics.

In The Last Decade

Fang Han

47 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Challenges of Big Data analysis

2014 819 citations Fang Han, Han Liu et al. profile →

Peers

Fang Han

SP

AI

MB

MIS

MSOR

Shohei Shimizu Japan

Marcus Hütter Australia

Han Liu China

Ivan Bratko Slovenia

Marco Zaffalon Switzerland

Deborah F. Swayne United States

Yijing Li China

Alan J. Miller United States

Christopher Meek United States

Mourad Oussalah Finland

Shohei Shimizu Japan

Fang Han

124 ×0.2

SP

1.0k ×2.2

AI

73 ×0.3

MB

37 ×0.3

MIS

155 ×1.4

MSOR

Citations per year, relative to Fang Han Fang Han (= 1×) peers Shohei Shimizu

Countries citing papers authored by Fang Han

Since Specialization

Citations

This map shows the geographic impact of Fang Han'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 Fang Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fang Han more than expected).

Fields of papers citing papers by Fang Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fang Han. 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 Fang Han. The network helps show where Fang Han may publish in the future.

Co-authorship network of co-authors of Fang Han

This figure shows the co-authorship network connecting the top 25 collaborators of Fang Han. A scholar is included among the top collaborators of Fang Han based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fang Han. Fang Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Han, Fang, et al.. (2024). On the failure of the bootstrap for Chatterjee’s rank correlation. Biometrika. 111(3). 1063–1070. 4 indexed citations

2.

Cattaneo, Matias D., et al.. (2024). On Rosenbaum’s rank-based matching estimator. Biometrika. 112(1).

3.

Shi, Hongjian, Mathias Drton, Marc Hallin, & Fang Han. (2024). Distribution-free tests of multivariate independence based on center-outward quadrant, Spearman, Kendall, and van der Waerden statistics. Bernoulli. 31(1). 1 indexed citations

4.

Fan, Yanqin, et al.. (2023). Estimation and inference in a high-dimensional semiparametric Gaussian copula vector autoregressive model. Journal of Econometrics. 237(1). 105513–105513. 3 indexed citations

5.

Huang, Qi, Bo‐Hao Tang, July Carolina Romero, et al.. (2022). Shell microelectrode arrays (MEAs) for brain organoids. Science Advances. 8(33). eabq5031–eabq5031. 111 indexed citations

6.

Wang, Guangxing, et al.. (2022). Robust Functional Principal Component Analysis via a Functional Pairwise Spatial Sign Operator. Biometrics. 79(2). 1239–1253. 7 indexed citations

7.

Zhang, Mengqi, et al.. (2022). IDEAS: individual level differential expression analysis for single-cell RNA-seq data. Genome biology. 23(1). 33–33. 29 indexed citations

8.

Gao, Chao, Fang Han, & Cun‐Hui Zhang. (2017). Minimax Risk Bounds for Piecewise Constant Models. arXiv (Cornell University). 3 indexed citations

9.

Han, Fang & Han Liu. (2016). Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution. Bernoulli. 23(1). 23–57. 22 indexed citations

10.

Han, Fang, et al.. (2015). Robust portfolio optimization. Neural Information Processing Systems. 28. 46–54. 6 indexed citations

11.

Han, Fang & Han Liu. (2013). Optimal Rates of Convergence of Transelliptical Component Analysis. arXiv (Cornell University). 1 indexed citations

12.

Han, Fang & Han Liu. (2013). Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model. Neural Information Processing Systems. 26. 1941–1949. 2 indexed citations

13.

Han, Fang & Han Liu. (2013). Transition Matrix Estimation in High Dimensional Time Series. International Conference on Machine Learning. 172–180. 26 indexed citations

14.

Han, Fang, Tuo Zhao, & Han Liu. (2013). CODA: high dimensional copula discriminant analysis. Journal of Machine Learning Research. 14(1). 629–671. 34 indexed citations

15.

Han, Fang & Han Liu. (2013). Principal Component Analysis on non-Gaussian Dependent Data. International Conference on Machine Learning. 240–248. 9 indexed citations

16.

Han, Fang & Han Liu. (2013). Scale-Invariant Sparse PCA on High-Dimensional Meta-Elliptical Data. Journal of the American Statistical Association. 109(505). 275–287. 28 indexed citations

17.

Wang, Zhaoran, Fang Han, & Han Liu. (2013). Sparse Principal Component Analysis for High Dimensional Multivariate Time Series. 31. 48–56. 6 indexed citations

18.

Liu, Han, Fang Han, & Cun‐Hui Zhang. (2012). Transelliptical Graphical Models. Neural Information Processing Systems. 25. 800–808. 28 indexed citations

19.

Han, Fang & Han Liu. (2012). Semiparametric Principal Component Analysis. Neural Information Processing Systems. 25. 171–179. 10 indexed citations

20.

Han, Fang & Han Liu. (2012). Transelliptical Component Analysis. Neural Information Processing Systems. 25. 359–367. 9 indexed citations

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.

Explore authors with similar magnitude of impact