J. S. Marron
- Statistics and Probability top 0.2%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Molecular Biology
- Control and Systems Engineering top 5%
- Co-authors
- Probal ChaudhuriByeong U. ParkSimon J. SheatherHaipeng ShenD. NolanCheolwoo ParkWolfgang Karl HärdleJianhua Z. Huang
- Topics
- Statistical Methods and Inference (16 papers)Morphological variations and asymmetry (10 papers)Data Visualization and Analytics (10 papers)
- Partner nations
- United StatesAustraliaGermany
In The Last Decade
J. S. Marron
70 papers receiving 2.6k citations
Peers
Comparison fields: 5 of 172
- Statistics and Probability 1.1k
- Artificial Intelligence 653
- Computer Vision and Pattern Recognition 379
- Molecular Biology 277
- Control and Systems Engineering 251
Countries citing papers authored by J. S. Marron
This map shows the geographic impact of J. S. Marron'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 J. S. Marron with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. S. Marron more than expected).
Fields of papers citing papers by J. S. Marron
This network shows the impact of papers produced by J. S. Marron. 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 J. S. Marron. The network helps show where J. S. Marron may publish in the future.
Co-authorship network of co-authors of J. S. Marron
This figure shows the co-authorship network connecting the top 25 collaborators of J. S. Marron. A scholar is included among the top collaborators of J. S. Marron 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 J. S. Marron. J. S. Marron 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 | 1 | |
| 3 | 15 | |
| 4 | 3 | |
| 5 | 10 | |
| 6 | 24 | |
| 7 | 13 | |
| 8 | 29 | |
| 9 | 18 | |
| 10 | 7 | |
| 11 | 41 | |
| 12 | 22 | |
| 13 | Local Likelihood SiZer Map | 14 |
| 14 | 75 | |
| 15 | The functional data analysis view of longitudinal data | 48 |
| 16 | 40 | |
| 17 | 11 | |
| 18 | 55 | |
| 19 | 0 | |
| 20 | 182 |
About J. S. Marron
J. S. Marron is a scholar working on Statistics and Probability, Computational Mathematics and Computer Vision and Pattern Recognition, having authored 77 papers that have together received 2.8k indexed citations. Recurring topics across this work include Statistical Methods and Inference (16 papers), Morphological variations and asymmetry (10 papers) and Data Visualization and Analytics (10 papers). The work is most often cited by research in Statistics and Probability (1.1k citations), Statistics, Probability and Uncertainty (219 citations) and Computational Mathematics (16 citations). J. S. Marron has collaborated with scholars based in United States, Australia and Germany. Frequent co-authors include Probal Chaudhuri, Byeong U. Park, Simon J. Sheather, Haipeng Shen, D. Nolan, Cheolwoo Park, Wolfgang Karl Härdle, Jianhua Z. Huang, Mihee Lee and Fred Godtliebsen. Their work appears in journals such as Nucleic Acids Research, Journal of the American Statistical Association and Biometrics.
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.