Nengxiang Ling

71 total papers · 638 total citations
36 papers, 450 citations indexed

About

Nengxiang Ling is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Nengxiang Ling has authored 36 papers receiving a total of 450 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Statistics and Probability, 16 papers in Artificial Intelligence and 8 papers in Management Science and Operations Research. Recurrent topics in Nengxiang Ling's work include Statistical Methods and Inference (28 papers), Bayesian Methods and Mixture Models (14 papers) and Advanced Statistical Methods and Models (8 papers). Nengxiang Ling is often cited by papers focused on Statistical Methods and Inference (28 papers), Bayesian Methods and Mixture Models (14 papers) and Advanced Statistical Methods and Models (8 papers). Nengxiang Ling collaborates with scholars based in China, France and Spain. Nengxiang Ling's co-authors include Philippe Vieu, Shuhe Hu, Wenzhi Yang, Germán Aneiros, Xuejun Wang, Xuejun Wang, Yuehua Wu, Xuejun Wang, Hui Ding and Yi Wu and has published in prestigious journals such as Journal of the Royal Statistical Society Series B (Statistical Methodology), Journal of Multivariate Analysis and Journal of Statistical Planning and Inference.

In The Last Decade

Nengxiang Ling

32 papers receiving 442 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Nengxiang Ling	372	183	127	72	34	36	450
Pi‐Erh Lin	370 1.0×	124 0.7×	106 0.8×	52 0.7×	60 1.8×	25	515
Eckhard Liebscher	307 0.8×	124 0.7×	97 0.8×	230 3.2×	28 0.8×	37	516
Miao Bai-qi	280 0.8×	109 0.6×	65 0.5×	122 1.7×	32 0.9×	47	513
Stephen G. Walker	292 0.8×	185 1.0×	51 0.4×	38 0.5×	24 0.7×	38	447
Radu Theodorescu	219 0.6×	93 0.5×	156 1.2×	127 1.8×	27 0.8×	59	511
Takemi Yanagimoto	300 0.8×	118 0.6×	103 0.8×	81 1.1×	9 0.3×	61	451
Volker Mammitzsch	287 0.8×	100 0.5×	53 0.4×	56 0.8×	79 2.3×	18	421
G.P. Steck	218 0.6×	118 0.6×	84 0.7×	41 0.6×	8 0.2×	26	421
Wai Kiu Chan	168 0.5×	93 0.5×	109 0.9×	49 0.7×	19 0.6×	22	471
Gérard Collomb	266 0.7×	87 0.5×	31 0.2×	78 1.1×	75 2.2×	28	414

Countries citing papers authored by Nengxiang Ling

Since Specialization

Citations

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

Fields of papers citing papers by Nengxiang Ling

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nengxiang Ling

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

All Works

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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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