Marco Riani

2.9k total citations
88 papers, 1.7k citations indexed

About

Marco Riani is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence. According to data from OpenAlex, Marco Riani has authored 88 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Statistics and Probability, 34 papers in Statistics, Probability and Uncertainty and 15 papers in Artificial Intelligence. Recurrent topics in Marco Riani's work include Advanced Statistical Methods and Models (56 papers), Advanced Statistical Process Monitoring (34 papers) and Statistical Methods and Inference (31 papers). Marco Riani is often cited by papers focused on Advanced Statistical Methods and Models (56 papers), Advanced Statistical Process Monitoring (34 papers) and Statistical Methods and Inference (31 papers). Marco Riani collaborates with scholars based in Italy, United Kingdom and Belgium. Marco Riani's co-authors include Anthony C. Atkinson, Andrea Cerioli, Aldo Corbellini, Domenico Perrotta, Andrew Harvey, Siem Jan Koopman, Alessio Farcomeni, Sergio Zani, Agustín Mayo-Íscar and Luis Ángel García-Escudero and has published in prestigious journals such as Technometrics, Chemosphere and Pattern Recognition.

In The Last Decade

Marco Riani

85 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Riani Italy 21 952 488 325 173 149 88 1.7k
Regina Y. Liu United States 23 1.6k 1.6× 898 1.8× 293 0.9× 230 1.3× 184 1.2× 44 2.3k
Juan Romo Spain 16 596 0.6× 240 0.5× 254 0.8× 169 1.0× 188 1.3× 58 1.2k
Ricardo Fraiman Uruguay 27 1.6k 1.7× 471 1.0× 777 2.4× 182 1.1× 143 1.0× 88 2.7k
Ruben H. Zamar Canada 19 1.2k 1.2× 659 1.4× 207 0.6× 93 0.5× 54 0.4× 51 1.6k
Sadanori Konishi Japan 20 898 0.9× 142 0.3× 469 1.4× 167 1.0× 111 0.7× 88 1.9k
Shuangzhe Liu Australia 22 578 0.6× 251 0.5× 194 0.6× 214 1.2× 200 1.3× 144 1.9k
Prem K. Goel United States 22 559 0.6× 264 0.5× 316 1.0× 189 1.1× 166 1.1× 78 2.0k
Manuel Febrero–Bande Spain 23 959 1.0× 276 0.6× 547 1.7× 157 0.9× 171 1.1× 66 2.6k
Kesar Singh United States 21 2.0k 2.1× 670 1.4× 534 1.6× 307 1.8× 131 0.9× 48 2.5k
Andrea Cerioli Italy 18 678 0.7× 304 0.6× 292 0.9× 54 0.3× 72 0.5× 45 971

Countries citing papers authored by Marco Riani

Since Specialization
Citations

This map shows the geographic impact of Marco Riani'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 Marco Riani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Riani more than expected).

Fields of papers citing papers by Marco Riani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marco Riani. 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 Marco Riani. The network helps show where Marco Riani may publish in the future.

Co-authorship network of co-authors of Marco Riani

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Riani. A scholar is included among the top collaborators of Marco Riani 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 Marco Riani. Marco Riani 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.
2.
Riani, Marco, et al.. (2024). Perbandingan Agglomerative Nesting dan K-Means untuk Klasterisasi Ketimpangan Gender berdasarkan Dimensi Kesehatan Reproduksi. Seminar Nasional Official Statistics. 2024(1). 459–470.
3.
Giudici, Paolo, Emanuela Raffinetti, & Marco Riani. (2024). Robust machine learning models: linear and nonlinear. International Journal of Data Science and Analytics. 20(2). 1043–1050. 5 indexed citations
4.
Bittelli, Marco, et al.. (2023). Robust Statistical Processing of Long-Time Data Series to Estimate Soil Water Content. Mathematical Geosciences. 56(1). 3–26. 2 indexed citations
5.
Riani, Marco, Anthony C. Atkinson, & Aldo Corbellini. (2023). Robust Transformations for Multiple Regression via Additivity and Variance Stabilization. Journal of Computational and Graphical Statistics. 33(1). 85–100. 2 indexed citations
6.
Atkinson, Anthony C., Marco Riani, & Aldo Corbellini. (2021). The box-cox transformation: review and extensions. London School of Economics and Political Science Research Online (London School of Economics and Political Science). 1 indexed citations
7.
Riani, Marco & Mia Hubert. (2021). Editorial, special issue on “Advances in Robust Statistics”. METRON. 79(2). 121–125. 1 indexed citations
8.
Spaggiari, Giulia, et al.. (2021). A synergism of in silico and statistical approaches to discover new potential endocrine disruptor mycotoxins. Toxicology and Applied Pharmacology. 435. 115832–115832. 4 indexed citations
9.
García-Escudero, Luis Ángel, Agustín Mayo-Íscar, & Marco Riani. (2021). Constrained parsimonious model-based clustering. Statistics and Computing. 32(1). 2–2. 11 indexed citations
10.
Cozzini, Pietro, et al.. (2021). Computational methods on food contact chemicals: Big data and in silico screening on nuclear receptors family. Chemosphere. 292. 133422–133422. 6 indexed citations
11.
Corbellini, Aldo, et al.. (2020). Tools for Monitoring Robust Regression in SAS IML Studio: S, MM, LTS, LMS and Especially the Forward Search. Joint Research Centre (European Commission). 1 indexed citations
12.
Riani, Marco, Aldo Corbellini, & Anthony C. Atkinson. (2018). The Use of Prior Information in Very Robust Regression for Fraud Detection. International Statistical Review. 86(2). 205–218. 1 indexed citations
13.
Cerioli, Andrea, Marco Riani, Anthony C. Atkinson, & Aldo Corbellini. (2017). The power of monitoring: how to make the most of a contaminated multivariate sample. Statistical Methods & Applications. 27(4). 559–587. 29 indexed citations
14.
Cerioli, Andrea, Alessio Farcomeni, & Marco Riani. (2014). Strong consistency and robustness of the Forward Search estimator of multivariate location and scatter. Journal of Multivariate Analysis. 126. 167–183. 20 indexed citations
15.
Riani, Marco, Andrea Cerioli, Anthony C. Atkinson, & Domenico Perrotta. (2014). Monitoring robust regression. Electronic Journal of Statistics. 8(1). 37 indexed citations
16.
Riani, Marco, et al.. (2011). Robust Analysis of Default Intensity. SSRN Electronic Journal. 1 indexed citations
17.
Riani, Marco. (2004). Extensions of the Forward Search to Time Series. Studies in Nonlinear Dynamics and Econometrics. 8(2). 14 indexed citations
18.
Riani, Marco & Anthony C. Atkinson. (2000). Robust Diagnostic Data Analysis: Transformations in Regression. Technometrics. 42(4). 384–394. 38 indexed citations
19.
Cerioli, Andrea & Marco Riani. (1999). The Ordering of Spatial Data and the Detection of Multiple Outliers. Journal of Computational and Graphical Statistics. 8(2). 239–258. 33 indexed citations
20.
Harvey, Andrew, Siem Jan Koopman, & Marco Riani. (1997). The Modeling and Seasonal Adjustment of Weekly Observations. Journal of Business and Economic Statistics. 15(3). 354–368. 50 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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