Roberto Rocci

906 total citations
40 papers, 539 citations indexed

About

Roberto Rocci is a scholar working on Artificial Intelligence, Signal Processing and Computational Mathematics. According to data from OpenAlex, Roberto Rocci has authored 40 papers receiving a total of 539 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 13 papers in Signal Processing and 12 papers in Computational Mathematics. Recurrent topics in Roberto Rocci's work include Bayesian Methods and Mixture Models (16 papers), Tensor decomposition and applications (12 papers) and Blind Source Separation Techniques (9 papers). Roberto Rocci is often cited by papers focused on Bayesian Methods and Mixture Models (16 papers), Tensor decomposition and applications (12 papers) and Blind Source Separation Techniques (9 papers). Roberto Rocci collaborates with scholars based in Italy, Netherlands and United States. Roberto Rocci's co-authors include Maurizio Vichi, Salvatore Ingrassia, Jos M. F. ten Berge, Antonello Maruotti, Stefano Antonio Gattone, Henk A. L. Kiers, Murray Aitkin, Paolo Giordani, Simone Borra and Nicholas D. Sidiropoulos and has published in prestigious journals such as Statistics in Medicine, Psychometrika and Chemometrics and Intelligent Laboratory Systems.

In The Last Decade

Roberto Rocci

38 papers receiving 509 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roberto Rocci Italy 14 269 157 112 83 63 40 539
Salvatore Ingrassia Italy 16 538 2.0× 406 2.6× 6 0.1× 38 0.5× 37 0.6× 47 692
Tõnu Kollo Estonia 10 136 0.5× 323 2.1× 7 0.1× 34 0.4× 14 0.2× 25 576
Abel Rodríguez United States 15 503 1.9× 344 2.2× 9 0.1× 55 0.7× 30 0.5× 60 891
Martina Mincheva United States 5 99 0.4× 362 2.3× 13 0.1× 68 0.8× 24 0.4× 7 734
Efstathia Bura United States 13 100 0.4× 344 2.2× 7 0.1× 23 0.3× 59 0.9× 37 586
Jacques Dauxois France 8 97 0.4× 273 1.7× 4 0.0× 31 0.4× 34 0.5× 14 423
M. Pourahmadi United States 13 240 0.9× 592 3.8× 7 0.1× 35 0.4× 15 0.2× 23 944
Xiangrong Yin United States 16 292 1.1× 795 5.1× 6 0.1× 43 0.5× 170 2.7× 59 1.1k
Yves Romain France 6 97 0.4× 259 1.6× 4 0.0× 31 0.4× 21 0.3× 16 393
L. R. Haff United States 9 116 0.4× 414 2.6× 7 0.1× 56 0.7× 38 0.6× 16 591

Countries citing papers authored by Roberto Rocci

Since Specialization
Citations

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

Fields of papers citing papers by Roberto Rocci

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roberto Rocci

This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Rocci. A scholar is included among the top collaborators of Roberto Rocci 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 Roberto Rocci. Roberto Rocci 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.
Rocci, Roberto, et al.. (2024). Generalized Reduced K–Means. Computational Statistics. 40(4). 1753–1778.
2.
Rocci, Roberto, et al.. (2024). Mixture models for simultaneous classification and reduction of three-way data. Computational Statistics. 40(1). 469–507. 1 indexed citations
3.
Rocci, Roberto, et al.. (2023). Composite likelihood methods for parsimonious model-based clustering of mixed-type data. Advances in Data Analysis and Classification. 18(2). 381–407. 1 indexed citations
4.
Rocci, Roberto, et al.. (2023). LASSO–penalized clusterwise linear regression modelling: a two–step approach. Journal of Statistical Computation and Simulation. 93(18). 3235–3258. 5 indexed citations
5.
Rocci, Roberto, et al.. (2023). Measuring the Recovery Performance of a Portfolio of NPLs. Computation. 11(2). 29–29. 1 indexed citations
6.
Giordani, Paolo, Roberto Rocci, & Giuseppe Bove. (2020). Factor Uniqueness of the Structural Parafac Model. Psychometrika. 85(3). 555–574. 3 indexed citations
7.
Rocci, Roberto, et al.. (2019). Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models. Statistical Methods & Applications. 29(1). 49–78. 2 indexed citations
8.
Rocci, Roberto, et al.. (2017). Clusterwise linear regression modeling with soft scale constraints. International Journal of Approximate Reasoning. 91. 160–178. 10 indexed citations
9.
Rocci, Roberto, et al.. (2017). A data driven equivariant approach to constrained Gaussian mixture modeling. Advances in Data Analysis and Classification. 12(2). 235–260. 8 indexed citations
10.
Rocci, Roberto, et al.. (2017). A Model-Based Approach to Simultaneous Clustering and Dimensional Reduction of Ordinal Data. Psychometrika. 82(4). 1007–1034. 1 indexed citations
11.
Giordani, Paolo & Roberto Rocci. (2017). Some clarifications of remedies for Candecomp/Parafac degeneracy by means of an SVD-penalized approach. Chemometrics and Intelligent Laboratory Systems. 162. 172–181. 1 indexed citations
12.
Rocci, Roberto, et al.. (2014). Mixture models for ordinal data: a pairwise likelihood approach. Statistics and Computing. 26(1-2). 529–547. 13 indexed citations
13.
Giordani, Paolo & Roberto Rocci. (2013). Constrained Candecomp/Parafac via the Lasso. Psychometrika. 78(4). 669–684. 10 indexed citations
14.
Giordani, Paolo & Roberto Rocci. (2013). Candecomp/Parafac with ridge regularization. Chemometrics and Intelligent Laboratory Systems. 129. 3–9. 5 indexed citations
15.
Maruotti, Antonello & Roberto Rocci. (2009). A semiparametric approach to mixed nonhomogeneous hidden Markov models. IRIS Research product catalog (Sapienza University of Rome). 1 indexed citations
16.
Rocci, Roberto, et al.. (2008). Assessing the default risk by means of a discrete‐time survival analysis approach. Applied Stochastic Models in Business and Industry. 24(4). 291–306. 16 indexed citations
17.
Zio, Marco Di, et al.. (2006). A mixture of mixture models for a classification problem: The unity measure error. Computational Statistics & Data Analysis. 51(5). 2573–2585. 23 indexed citations
18.
Berge, Jos M. F. ten, Nicholas D. Sidiropoulos, & Roberto Rocci. (2004). Typical rank and indscal dimensionality for symmetric three-way arrays of order I×2×2 or I×3×3. Linear Algebra and its Applications. 388. 363–377. 27 indexed citations
19.
Aitkin, Murray & Roberto Rocci. (2002). A general maximum likelihood analysis of measurement error in generalized linear models. Statistics and Computing. 12(2). 163–174. 30 indexed citations
20.
Rocci, Roberto. (1992). Three-mode factor analysis with binary core and orthonormality constraints. Statistical Methods & Applications. 1(3). 413–422. 7 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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