Nicola Torelli

2.0k total citations · 2 hit papers
35 papers, 1.2k citations indexed

About

Nicola Torelli is a scholar working on Economics and Econometrics, Statistics and Probability and Artificial Intelligence. According to data from OpenAlex, Nicola Torelli has authored 35 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Economics and Econometrics, 10 papers in Statistics and Probability and 8 papers in Artificial Intelligence. Recurrent topics in Nicola Torelli's 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). Nicola Torelli is often cited by papers focused on Statistical Methods and Bayesian Inference (7 papers), Bayesian Methods and Mixture Models (6 papers) and Labor market dynamics and wage inequality (5 papers). Nicola Torelli collaborates with scholars based in Italy, Switzerland and France. Nicola Torelli's co-authors include Giovanna Menardi, Nicola Lunardon, Adelchi Azzalini, Ugo Trivellato, Fabrizio Durante, Francesco Pauli, Jerry Polesel, Fabio Levi, Giovanni Franchin and Luigino Dal Maso and has published in prestigious journals such as Journal of Econometrics, Physics in Medicine and Biology and European Economic Review.

In The Last Decade

Nicola Torelli

33 papers receiving 1.2k citations

Hit Papers

Training and assessing classification rules with imbalanc... 2012 2026 2016 2021 2012 2014 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nicola Torelli Italy 10 409 171 94 89 79 35 1.2k
Giovanna Menardi Italy 8 420 1.0× 64 0.4× 97 1.0× 55 0.6× 75 0.9× 24 1.0k
Erik Štrumbelj Slovenia 16 716 1.8× 389 2.3× 102 1.1× 63 0.7× 57 0.7× 39 2.2k
Justin Bleich United States 7 542 1.3× 92 0.5× 53 0.6× 112 1.3× 58 0.7× 10 1.6k
Daniel T. Larose United States 10 358 0.9× 71 0.4× 55 0.6× 59 0.7× 62 0.8× 14 1.2k
Adam Kapelner United States 13 524 1.3× 104 0.6× 96 1.0× 133 1.5× 49 0.6× 31 1.9k
Laura Toloşi Germany 6 391 1.0× 51 0.3× 258 2.7× 46 0.5× 87 1.1× 10 2.0k
Fernando Jiménez Spain 18 462 1.1× 71 0.4× 39 0.4× 54 0.6× 96 1.2× 87 1.2k
Mohammad T. Khasawneh United States 20 244 0.6× 161 0.9× 51 0.5× 55 0.6× 71 0.9× 117 1.4k
Asja Fischer Germany 21 784 1.9× 194 1.1× 50 0.5× 29 0.3× 95 1.2× 66 1.9k
Pedro Henriques Abreu Portugal 20 895 2.2× 48 0.3× 131 1.4× 76 0.9× 148 1.9× 82 1.7k

Countries citing papers authored by Nicola Torelli

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Volken, W., et al.. (2023). Efficiency enhancements of a Monte Carlo beamlet based treatment planning process: implementation and parameter study. Physics in Medicine and Biology. 68(4). 44003–44003. 7 indexed citations
2.
Pauli, Francesco, et al.. (2021). Avoiding prior–data conflict in regression models via mixture priors. Canadian Journal of Statistics. 50(2). 491–510. 9 indexed citations
3.
Torelli, Nicola, et al.. (2020). Comparing Goal-Based and Result-Based Approaches in Modelling Football Outcomes. Social Indicators Research. 156(2-3). 801–813. 3 indexed citations
4.
Pauli, Francesco, et al.. (2018). Combining historical data and bookmakers’ odds in modelling football scores. ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/). 11 indexed citations
5.
Torelli, Nicola, et al.. (2017). A Comparison of Hierarchical Bayesian Models for Small Area Estimation of Counts. Open Journal of Statistics. 7(3). 521–550. 7 indexed citations
6.
Maso, Luigino Dal, Nicola Torelli, Matteo Di Maso, et al.. (2015). Combined effect of tobacco smoking and alcohol drinking in the risk of head and neck cancers: a re-analysis of case–control studies using bi-dimensional spline models. European Journal of Epidemiology. 31(4). 385–393. 61 indexed citations
7.
Lunardon, Nicola, Giovanna Menardi, & Nicola Torelli. (2014). ROSE: a Package for Binary Imbalanced Learning. The R Journal. 6(1). 79–79. 361 indexed citations breakdown →
8.
Durante, Fabrizio, et al.. (2014). Clustering of time series via non-parametric tail dependence estimation. Statistical Papers. 56(3). 701–721. 32 indexed citations
9.
Lunardon, Nicola, Giovanna Menardi, & Nicola Torelli. (2013). R package 'ROSE': Random Over-Sampling Examples. Research Padua Archive (University of Padua). 2 indexed citations
10.
Menardi, Giovanna & Nicola Torelli. (2013). The effect of training set selection when predicting defaulting small and medium-sized enterprises with unbalanced data. The Journal of Credit Risk. 9(4). 47–62. 1 indexed citations
11.
Durante, Fabrizio, et al.. (2013). Clustering of financial time series in risky scenarios. Advances in Data Analysis and Classification. 8(4). 359–376. 37 indexed citations
12.
Torelli, Nicola, et al.. (2012). Small area estimation: An application of a flexible fay-herriot method. Journal of Agricultural Science and Technology. 14(1). 75–85. 1 indexed citations
13.
Zio, Marco Di, et al.. (2008). File Concatenation of Survey Data: a Computer Intensive Approach to Sampling Weights Estimation. RePEc: Research Papers in Economics. 10(2). 5–12. 1 indexed citations
14.
Torelli, Nicola, et al.. (2008). Labour Force Estimates for Small Geographical Domains in Italy: Problems, Data and Models. RePEc: Research Papers in Economics. 116(4). 443–464. 1 indexed citations
15.
Torelli, Nicola, et al.. (2006). Metodi statistici per l'integrazione di dati da fonti diverse. ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/). 3 indexed citations
16.
Torelli, Nicola, et al.. (2006). Comparing hierarchical Bayesian models for small area estimation. ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/). 17–36. 1 indexed citations
17.
Paggiaro, Adriano & Nicola Torelli. (2004). The effect of classification errors in survival data analysis. Statistical Methods & Applications. 13(2). 4 indexed citations
18.
Azzalini, Adelchi, et al.. (2003). Detecting clusters via nonparametric density estimation. 1–4. 1 indexed citations
19.
Rettore, Enrico, Nicola Torelli, & Ugo Trivellato. (1990). Unemployment and Search for Work: Exploratory Analyses of Labour Market Attachment using CPS‐Type Data. Labour. 4(3). 161–190. 2 indexed citations
20.
Torelli, Nicola & Ugo Trivellato. (1988). Modelling Job‐Search Duration from the Italian Labour Force Data. Labour. 2(1). 117–134. 3 indexed citations

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.

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