Kang Ling James

683 citations
11 papers · 492 indexed · h-index 6
Topics
Statistical Methods and Inference (5 papers)Statistical Methods and Bayesian Inference (2 papers)Bayesian Methods and Mixture Models (2 papers)
Partner nations
BrazilUnited States

In The Last Decade

Kang Ling James

10 papers receiving 448 citations

Peers

Kang Ling James
Comparison fields: 5 of 90
  • Statistics and Probability 281
  • Statistics, Probability and Uncertainty 155
  • Artificial Intelligence 114
  • Management Science and Operations Research 44
  • Finance 43
Replace Norberto Corral with:
Norberto Corral Spain
Biao Zhang United States
R. Campo United States
Naif Alotaibi Saudi Arabia
Alessandra Salvan Italy
Elı́as Moreno Spain
Liuquan Sun China
Ali Gannoun France
Jooyong Shim South Korea
Kang Ling James relative to Norberto Corral Spain Norberto Corral's profile →
Citations per field
00.5×3.3×
Norberto Corral · 1×
Citations per year

Countries citing papers authored by Kang Ling James

Since Specialization
Citations

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

Fields of papers citing papers by Kang Ling James

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kang Ling James

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

All Works

11 of 11 papers shown
#WorkIndexed citations
1 1
2
Penalized Model-Based Clustering
2
3 20
4 16
5 279
6 1
7 12
8 151
9 0
10 3
11 7

About Kang Ling James

Kang Ling James is a scholar working on Statistics and Probability, Management Science and Operations Research and Finance, having authored 11 papers that have together received 492 indexed citations. Recurring topics across this work include Statistical Methods and Inference (5 papers), Statistical Methods and Bayesian Inference (2 papers) and Bayesian Methods and Mixture Models (2 papers). The work is most often cited by research in Statistics and Probability (281 citations), Statistics, Probability and Uncertainty (155 citations) and Health Information Management (36 citations). Kang Ling James has collaborated with scholars based in Brazil and United States. Frequent co-authors include Barry R. James, Sam Wieand, Mitchell H. Gail, David Siegmund, Yongcheng Qi, Wei Zhang and Fatima Anjum. Their work appears in journals such as Journal of the American Statistical Association, Biometrika and The Annals of Probability.

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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