S. Gànesalingam
- Artificial Intelligence top 10%
- Statistics and Probability top 2%
- Statistics, Probability and Uncertainty top 5%
- Industrial and Manufacturing Engineering top 5%
- Management Science and Operations Research top 10%
- Co-authors
- Geoffrey J. McLachlanK. GovindarajuJose L. TongzonMartin L. HazeltonKuldeep KumarCharles LawokoGraham R. WoodM.A. Nichols
- Topics
- Advanced Statistical Methods and Models (13 papers)Bayesian Methods and Mixture Models (11 papers)Statistical Methods and Inference (7 papers)
- Cited by
- Statistics and ProbabilityStatistics, Probability and UncertaintyIndustrial and Manufacturing Engineering
- Journals
- TechnometricsBiometricsBiometrika
- Partner nations
- New ZealandAustraliaSri Lanka
In The Last Decade
S. Gànesalingam
21 papers receiving 316 citations
Peers
Comparison fields: 5 of 66
- Artificial Intelligence 161
- Statistics and Probability 147
- Statistics, Probability and Uncertainty 88
- Industrial and Manufacturing Engineering 82
- Management Science and Operations Research 58
Countries citing papers authored by S. Gànesalingam
This map shows the geographic impact of S. Gànesalingam'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 S. Gànesalingam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Gànesalingam more than expected).
Fields of papers citing papers by S. Gànesalingam
This network shows the impact of papers produced by S. Gànesalingam. 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 S. Gànesalingam. The network helps show where S. Gànesalingam may publish in the future.
Co-authorship network of co-authors of S. Gànesalingam
This figure shows the co-authorship network connecting the top 25 collaborators of S. Gànesalingam. A scholar is included among the top collaborators of S. Gànesalingam 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 S. Gànesalingam. S. Gànesalingam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 6 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | Neural Network Vs Discriminant Analysis in Detecting Financial Distress Among Major Australian Companies | 3 |
| 8 | 8 | |
| 9 | 72 | |
| 10 | 39 | |
| 11 | 32 | |
| 12 | 2 | |
| 13 | 22 | |
| 14 | 0 | |
| 15 | 10 | |
| 16 | 12 | |
| 17 | 32 | |
| 18 | 14 | |
| 19 | 53 | |
| 20 | 2 |
About S. Gànesalingam
S. Gànesalingam is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty, having authored 22 papers that have together received 339 indexed citations. Recurring topics across this work include Advanced Statistical Methods and Models (13 papers), Bayesian Methods and Mixture Models (11 papers) and Statistical Methods and Inference (7 papers). The work is most often cited by research in Statistics and Probability (147 citations), Statistics, Probability and Uncertainty (88 citations) and Industrial and Manufacturing Engineering (82 citations). S. Gànesalingam has collaborated with scholars based in New Zealand, Australia and Sri Lanka. Frequent co-authors include Geoffrey J. McLachlan, K. Govindaraju, Jose L. Tongzon, Martin L. Hazelton, Kuldeep Kumar, Charles Lawoko, Graham R. Wood, M.A. Nichols, Brian Corbitt and A. Jonathan R. Godfrey. Their work appears in journals such as Technometrics, Biometrics and Biometrika.
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.