Marc Raimondo
- Computer Vision and Pattern Recognition top 5%
- Statistics and Probability top 2%
- Computational Mechanics top 10%
- Applied Mathematics top 5%
- Finance top 10%
- Co-authors
- Iain M. JohnstoneGérard KerkyacharianDavid L. DonohoDominique PicardNader TajvidiLaurent CavalierRafał KulikPeter Hall
- Topics
- Image and Signal Denoising Methods (10 papers)Statistical Methods and Inference (5 papers)Statistical and numerical algorithms (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Signal ProcessingJournal of the Royal Statistical Society Series B (Statistical Methodology)
- Partner nations
- AustraliaUnited StatesFrance
In The Last Decade
Marc Raimondo
16 papers receiving 326 citations
Peers
Comparison fields: 5 of 57
- Computer Vision and Pattern Recognition 161
- Statistics and Probability 149
- Computational Mechanics 77
- Applied Mathematics 65
- Finance 63
Countries citing papers authored by Marc Raimondo
This map shows the geographic impact of Marc Raimondo'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 Marc Raimondo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc Raimondo more than expected).
Fields of papers citing papers by Marc Raimondo
This network shows the impact of papers produced by Marc Raimondo. 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 Marc Raimondo. The network helps show where Marc Raimondo may publish in the future.
Co-authorship network of co-authors of Marc Raimondo
This figure shows the co-authorship network connecting the top 25 collaborators of Marc Raimondo. A scholar is included among the top collaborators of Marc Raimondo 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 Marc Raimondo. Marc Raimondo 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 | 20 | |
| 3 | 15 | |
| 4 | L p -WAVELET REGRESSION WITH CORRELATED ERRORS AND INVERSE PROBLEMS | 4 |
| 5 | The WaveD Transform in R: Performs Fast Translation-Invariant Wavelet Deconvolution | 1 |
| 6 | 7 | |
| 7 | 23 | |
| 8 | 22 | |
| 9 | A peaks over threshold model for change-point detection by wavelets | 37 |
| 10 | 29 | |
| 11 | 26 | |
| 12 | 96 | |
| 13 | 11 | |
| 14 | 61 | |
| 15 | 8 | |
| 16 | 7 |
About Marc Raimondo
Marc Raimondo is a scholar working on Statistics and Probability, Applied Mathematics and Computer Vision and Pattern Recognition, having authored 16 papers that have together received 368 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (10 papers), Statistical Methods and Inference (5 papers) and Statistical and numerical algorithms (3 papers). The work is most often cited by research in Statistics and Probability (149 citations), Computer Vision and Pattern Recognition (161 citations) and Finance (63 citations). Marc Raimondo has collaborated with scholars based in Australia, United States and France. Frequent co-authors include Iain M. Johnstone, Gérard Kerkyacharian, David L. Donoho, Dominique Picard, Nader Tajvidi, Laurent Cavalier, Rafał Kulik, Peter Hall, Ming−Yen Cheng and Michael Stewart. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Signal Processing and Journal of the Royal Statistical Society Series B (Statistical Methodology).
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.