Mohan Delampady
- Statistics and Probability top 1%
- Artificial Intelligence top 10%
- Statistics, Probability and Uncertainty top 2%
- Ecology top 10%
- Management Science and Operations Research top 5%
- Co-authors
- James O. BergerArjun M. GopalaswamyT. K. SamantaDavid W. MacdonaldJayanta K. GhoshK. Ullas KaranthJames D. NicholsJ. Andrew Royle
- Topics
- Statistical Methods and Inference (10 papers)Advanced Statistical Methods and Models (8 papers)Bayesian Methods and Mixture Models (7 papers)
- Partner nations
- IndiaUnited StatesCanada
In The Last Decade
Mohan Delampady
32 papers receiving 720 citations
Peers
Comparison fields: 5 of 116
- Statistics and Probability 433
- Artificial Intelligence 176
- Statistics, Probability and Uncertainty 160
- Ecology 147
- Management Science and Operations Research 89
Countries citing papers authored by Mohan Delampady
This map shows the geographic impact of Mohan Delampady'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 Mohan Delampady with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohan Delampady more than expected).
Fields of papers citing papers by Mohan Delampady
This network shows the impact of papers produced by Mohan Delampady. 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 Mohan Delampady. The network helps show where Mohan Delampady may publish in the future.
Co-authorship network of co-authors of Mohan Delampady
This figure shows the co-authorship network connecting the top 25 collaborators of Mohan Delampady. A scholar is included among the top collaborators of Mohan Delampady 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 Mohan Delampady. Mohan Delampady is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 18 | |
| 2 | BAYESIAN NONPARAMETRIC REGRESSION USING WAVELETS | 1 |
| 3 | HIERARCHICAL BAYESIAN CURVE FITTING AND MODEL CHOICE FOR SPATIAL DATA | 1 |
| 4 | 45 | |
| 5 | 1 | |
| 6 | 67 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | A Hierarchical Bayesian Approach to the Estimation of Monotone Hazard Rates in the Random Right Censorship Model | 1 |
| 10 | 3 | |
| 11 | 7 | |
| 12 | 4 | |
| 13 | Probability and Statistics | 1 |
| 14 | 12 | |
| 15 | 4 | |
| 16 | 28 | |
| 17 | 21 | |
| 18 | 8 | |
| 19 | 9 | |
| 20 | 435 |
About Mohan Delampady
Mohan Delampady is a scholar working on Statistics and Probability, Ecological Modeling and Statistics, Probability and Uncertainty, having authored 34 papers that have together received 782 indexed citations. Recurring topics across this work include Statistical Methods and Inference (10 papers), Advanced Statistical Methods and Models (8 papers) and Bayesian Methods and Mixture Models (7 papers). The work is most often cited by research in Statistics and Probability (433 citations), Statistics, Probability and Uncertainty (160 citations) and Ecological Modeling (58 citations). Mohan Delampady has collaborated with scholars based in India, United States and Canada. Frequent co-authors include James O. Berger, Arjun M. Gopalaswamy, T. K. Samanta, David W. Macdonald, Jayanta K. Ghosh, K. Ullas Karanth, James D. Nichols, J. Andrew Royle, N. Samba Kumar and K. Ullas Karanth. Their work appears in journals such as Journal of the American Statistical Association, Ecology and The Annals of Statistics.
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.