Xianchao Xie

469 total citations
12 papers, 250 citations indexed

About

Xianchao Xie is a scholar working on Artificial Intelligence, Statistics and Probability and Computational Theory and Mathematics. According to data from OpenAlex, Xianchao Xie has authored 12 papers receiving a total of 250 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Statistics and Probability and 3 papers in Computational Theory and Mathematics. Recurrent topics in Xianchao Xie's work include Statistical Methods and Inference (5 papers), Bayesian Modeling and Causal Inference (4 papers) and Statistical Methods and Bayesian Inference (2 papers). Xianchao Xie is often cited by papers focused on Statistical Methods and Inference (5 papers), Bayesian Modeling and Causal Inference (4 papers) and Statistical Methods and Bayesian Inference (2 papers). Xianchao Xie collaborates with scholars based in China, United States and Hong Kong. Xianchao Xie's co-authors include Zhi Geng, Xiao‐Li Meng, S. C. Kou, Lawrence D. Brown, Zongming Ma, Shuicheng Yan, James T. Kwok, Thomas S. Huang, Lawrence Brown and Zhi Geng and has published in prestigious journals such as Journal of the American Statistical Association, Journal of the Royal Statistical Society Series B (Statistical Methodology) and The Annals of Statistics.

In The Last Decade

Xianchao Xie

12 papers receiving 235 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Xianchao Xie China 8 154 116 33 33 27 12 250
Alistair Stewart United States 6 109 0.7× 42 0.4× 29 0.9× 21 0.6× 12 0.4× 21 178
Jasper De Bock Belgium 8 108 0.7× 26 0.2× 49 1.5× 51 1.5× 23 0.9× 39 193
Tengyuan Liang United States 8 96 0.6× 67 0.6× 7 0.2× 24 0.7× 28 1.0× 24 211
Daniil Ryabko France 8 86 0.6× 19 0.2× 44 1.3× 23 0.7× 31 1.1× 31 137
Hei Chan United States 7 148 1.0× 22 0.2× 22 0.7× 36 1.1× 5 0.2× 12 187
Wouter M. Koolen Netherlands 8 120 0.8× 60 0.5× 16 0.5× 108 3.3× 5 0.2× 36 203
Silvia Acid Spain 7 194 1.3× 17 0.1× 36 1.1× 56 1.7× 7 0.3× 9 266
Hajime Yamato Japan 9 192 1.2× 222 1.9× 9 0.3× 36 1.1× 4 0.1× 42 329
M. C. Spruill United States 8 67 0.4× 128 1.1× 42 1.3× 71 2.2× 6 0.2× 28 265
James Hannan United States 9 69 0.4× 166 1.4× 10 0.3× 52 1.6× 9 0.3× 20 252

Countries citing papers authored by Xianchao Xie

Since Specialization
Citations

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

Fields of papers citing papers by Xianchao Xie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xianchao Xie

This figure shows the co-authorship network connecting the top 25 collaborators of Xianchao Xie. A scholar is included among the top collaborators of Xianchao Xie 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 Xianchao Xie. Xianchao Xie is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Xie, Xianchao, S. C. Kou, & Lawrence Brown. (2016). Optimal shrinkage estimation of mean parameters in family of distributions with quadratic variance. The Annals of Statistics. 44(2). 564–597. 10 indexed citations
2.
3.
Meng, Xiao‐Li & Xianchao Xie. (2013). I Got More Data, My Model is More Refined, but My Estimator is Getting Worse! Am I Just Dumb?. Econometric Reviews. 33(1-4). 218–250. 13 indexed citations
4.
Xie, Xianchao, S. C. Kou, & Lawrence D. Brown. (2012). SURE Estimates for a Heteroscedastic Hierarchical Model. Journal of the American Statistical Association. 107(500). 1465–1479. 43 indexed citations
5.
Brown, Lawrence D., Hui Nie, & Xianchao Xie. (2011). Ensemble Minimax Estimation for Multivariate Normal Means. The Annals of Statistics. 1. 4 indexed citations
6.
Xie, Xianchao & Zhi Geng. (2009). Collapsibility for Directed Acyclic Graphs. Scandinavian Journal of Statistics. 36(2). 185–203. 6 indexed citations
7.
Xie, Xianchao & Zhi Geng. (2008). A Recursive Method for Structural Learning of Directed Acyclic Graphs. Journal of Machine Learning Research. 9(14). 459–483. 47 indexed citations
8.
Wang, Xueli, et al.. (2008). Detecting multiple confounders. Journal of Statistical Planning and Inference. 139(3). 1073–1081. 2 indexed citations
9.
Xie, Xianchao, Shuicheng Yan, James T. Kwok, & Thomas S. Huang. (2008). Matrix-Variate Factor Analysis and Its Applications. IEEE Transactions on Neural Networks. 19(10). 1821–1826. 21 indexed citations
10.
Ma, Zongming, Xianchao Xie, & Zhi Geng. (2008). Structural Learning of Chain Graphs via Decomposition.. PubMed. 9. 2847–2880. 33 indexed citations
11.
Xie, Xianchao, et al.. (2006). Decomposition of structural learning about directed acyclic graphs. Artificial Intelligence. 170(4-5). 422–439. 31 indexed citations
12.
Ma, Zongming, Xianchao Xie, & Zhi Geng. (2005). Collapsibility of Distribution Dependence. Journal of the Royal Statistical Society Series B (Statistical Methodology). 68(1). 127–133. 7 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.

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