Anna Maria Paganoni
- Statistics and Probability top 2%
- Artificial Intelligence top 5%
- Immunology
- Cardiology and Cardiovascular Medicine top 10%
- Surgery
- Co-authors
- Francesca IevaPiercesare SecchiChiara MasciAlberto BarchielliLaura M. SangalliTommaso AgasistiValeria VitelliDavide Pigoli
- Topics
- Statistical Methods and Inference (25 papers)Advanced Statistical Methods and Models (14 papers)Bayesian Methods and Mixture Models (11 papers)
- Partner nations
- ItalyUnited KingdomSpain
In The Last Decade
Anna Maria Paganoni
103 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 157
- Statistics and Probability 259
- Artificial Intelligence 228
- Immunology 203
- Cardiology and Cardiovascular Medicine 179
- Surgery 179
Countries citing papers authored by Anna Maria Paganoni
This map shows the geographic impact of Anna Maria Paganoni'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 Anna Maria Paganoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anna Maria Paganoni more than expected).
Fields of papers citing papers by Anna Maria Paganoni
This network shows the impact of papers produced by Anna Maria Paganoni. 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 Anna Maria Paganoni. The network helps show where Anna Maria Paganoni may publish in the future.
Co-authorship network of co-authors of Anna Maria Paganoni
This figure shows the co-authorship network connecting the top 25 collaborators of Anna Maria Paganoni. A scholar is included among the top collaborators of Anna Maria Paganoni 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 Anna Maria Paganoni. Anna Maria Paganoni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 3 | |
| 3 | 6 | |
| 4 | 10 | |
| 5 | 37 | |
| 6 | Analysis of dropout in engineering BSc using logistic mixed-effect models. | 2 |
| 7 | 56 | |
| 8 | 3 | |
| 9 | 124 | |
| 10 | Covariance-based clustering in multivariate and functional data analysis | 5 |
| 11 | 5 | |
| 12 | 42 | |
| 13 | 5 | |
| 14 | 45 | |
| 15 | 3 | |
| 16 | 7 | |
| 17 | 16 | |
| 18 | 41 | |
| 19 | On a two-color generalized Polya urn | 14 |
| 20 | 21 |
About Anna Maria Paganoni
Anna Maria Paganoni is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Computer Science Applications, having authored 108 papers that have together received 1.4k indexed citations. Recurring topics across this work include Statistical Methods and Inference (25 papers), Advanced Statistical Methods and Models (14 papers) and Bayesian Methods and Mixture Models (11 papers). The work is most often cited by research in Statistics and Probability (259 citations), Immunology (203 citations) and Family Practice (21 citations). Anna Maria Paganoni has collaborated with scholars based in Italy, United Kingdom and Spain. Frequent co-authors include Francesca Ieva, Piercesare Secchi, Chiara Masci, Alberto Barchielli, Laura M. Sangalli, Tommaso Agasisti, Valeria Vitelli, Davide Pigoli, Simonetta Scalvini and Maria Pia Protti. Their work appears in journals such as Journal of Clinical Oncology, The Journal of Immunology and PLoS ONE.
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.