Nicola Torelli
- Artificial Intelligence top 5%
- Economics and Econometrics top 5%
- Molecular Biology
- Statistics and Probability top 5%
- Electrical and Electronic Engineering
- Co-authors
- Giovanna MenardiNicola LunardonAdelchi AzzaliniUgo TrivellatoFabrizio DuranteFrancesco PauliMatteo Di MasoJerry Polesel
- Topics
- Statistical Methods and Bayesian Inference (7 papers)Bayesian Methods and Mixture Models (6 papers)Labor market dynamics and wage inequality (5 papers)
- Partner nations
- ItalySwitzerlandCyprus
In The Last Decade
Nicola Torelli
33 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Artificial Intelligence 409
- Economics and Econometrics 171
- Molecular Biology 94
- Statistics and Probability 89
- Electrical and Electronic Engineering 79
Countries citing papers authored by Nicola Torelli
This map shows the geographic impact of Nicola Torelli'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 Nicola Torelli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicola Torelli more than expected).
Fields of papers citing papers by Nicola Torelli
This network shows the impact of papers produced by Nicola Torelli. 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 Nicola Torelli. The network helps show where Nicola Torelli may publish in the future.
Co-authorship network of co-authors of Nicola Torelli
This figure shows the co-authorship network connecting the top 25 collaborators of Nicola Torelli. A scholar is included among the top collaborators of Nicola Torelli 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 Nicola Torelli. Nicola Torelli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 9 | |
| 3 | 3 | |
| 4 | 11 | |
| 5 | 7 | |
| 6 | 61 | |
| 7 | 32 | |
| 8 | ROSE: a Package for Binary Imbalanced Learningbreakdown → | 361 |
| 9 | R package 'ROSE': Random Over-Sampling Examples | 2 |
| 10 | 1 | |
| 11 | 12 | |
| 12 | Small area estimation: An application of a flexible fay-herriot method | 1 |
| 13 | 4 | |
| 14 | Labour Force Estimates for Small Geographical Domains in Italy: Problems, Data and Models | 1 |
| 15 | File Concatenation of Survey Data: a Computer Intensive Approach to Sampling Weights Estimation | 1 |
| 16 | Metodi statistici per l'integrazione di dati da fonti diverse | 3 |
| 17 | 4 | |
| 18 | Detecting clusters via nonparametric density estimation | 1 |
| 19 | 48 | |
| 20 | 1 |
About Nicola Torelli
Nicola Torelli is a scholar working on Statistics and Probability, Economics and Econometrics and Management Science and Operations Research, having authored 35 papers that have together received 1.2k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (7 papers), Bayesian Methods and Mixture Models (6 papers) and Labor market dynamics and wage inequality (5 papers). The work is most often cited by research in Artificial Intelligence (409 citations), Health Information Management (59 citations) and Statistics and Probability (89 citations). Nicola Torelli has collaborated with scholars based in Italy, Switzerland and Cyprus. Frequent co-authors include Giovanna Menardi, Nicola Lunardon, Adelchi Azzalini, Ugo Trivellato, Fabrizio Durante, Francesco Pauli, Matteo Di Maso, Jerry Polesel, Carlo La Vecchia and Fabio Levi. Their work appears in journals such as Journal of Econometrics, Physics in Medicine and Biology and European Economic Review.
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.