Rosa E. Lillo

1.5k total citations
91 papers, 872 citations indexed

About

Rosa E. Lillo is a scholar working on Statistics and Probability, Management Information Systems and Management Science and Operations Research. According to data from OpenAlex, Rosa E. Lillo has authored 91 papers receiving a total of 872 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Statistics and Probability, 24 papers in Management Information Systems and 24 papers in Management Science and Operations Research. Recurrent topics in Rosa E. Lillo's work include Advanced Queuing Theory Analysis (24 papers), Probability and Risk Models (19 papers) and Statistical Distribution Estimation and Applications (18 papers). Rosa E. Lillo is often cited by papers focused on Advanced Queuing Theory Analysis (24 papers), Probability and Risk Models (19 papers) and Statistical Distribution Estimation and Applications (18 papers). Rosa E. Lillo collaborates with scholars based in Spain, Italy and France. Rosa E. Lillo's co-authors include Michael P. Wiper, Juan Romo, Pepa Ramírez‐Cobo, Moshe Shaked, Pedro Galeano, Belén Martín-Barragán, Nuria Torrado, Félix Belzunce, José M. Ruiz and M. Concepción Ausín and has published in prestigious journals such as Technometrics, Scientific Reports and European Journal of Operational Research.

In The Last Decade

Rosa E. Lillo

87 papers receiving 842 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rosa E. Lillo Spain 16 388 187 180 177 147 91 872
James Lynch United States 14 362 0.9× 216 1.2× 212 1.2× 117 0.7× 218 1.5× 48 1.1k
R.Y. Rubinstein Israel 17 119 0.3× 142 0.8× 275 1.5× 136 0.8× 121 0.8× 31 848
Erwin Straub Switzerland 3 532 1.4× 326 1.7× 298 1.7× 91 0.5× 426 2.9× 5 1.0k
Henry Lam United States 15 176 0.5× 202 1.1× 462 2.6× 204 1.2× 208 1.4× 116 1.2k
S. N. U. A. Kirmani United States 16 694 1.8× 356 1.9× 213 1.2× 110 0.6× 178 1.2× 55 1.1k
Min‐Te Chao Taiwan 12 567 1.5× 208 1.1× 183 1.0× 224 1.3× 218 1.5× 19 1.0k
Christos Alexopoulos United States 14 213 0.5× 249 1.3× 365 2.0× 64 0.4× 32 0.2× 93 757
Savaş Dayanik United States 15 168 0.4× 299 1.6× 468 2.6× 260 1.5× 47 0.3× 34 1.1k
Wheyming Tina Song Taiwan 13 188 0.5× 212 1.1× 502 2.8× 67 0.4× 31 0.2× 57 874
Jayaram Sethuraman United States 16 583 1.5× 342 1.8× 226 1.3× 130 0.7× 446 3.0× 54 1.1k

Countries citing papers authored by Rosa E. Lillo

Since Specialization
Citations

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

Fields of papers citing papers by Rosa E. Lillo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rosa E. Lillo. 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 Rosa E. Lillo. The network helps show where Rosa E. Lillo may publish in the future.

Co-authorship network of co-authors of Rosa E. Lillo

This figure shows the co-authorship network connecting the top 25 collaborators of Rosa E. Lillo. A scholar is included among the top collaborators of Rosa E. Lillo 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 Rosa E. Lillo. Rosa E. Lillo 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.
Meucci, Maria Chiara, Rosa E. Lillo, Emanuele Monda, et al.. (2025). Patterns of Left Ventricular Remodeling and Outcomes in Fabry Disease. Journal of the American Society of Echocardiography. 39(3). 318–320.
2.
Guglielmino, Valeria, Marco Ceccanti, Maurizio Inghilleri, et al.. (2025). Polyneuropathy in Wild‐Type Transthyretin Amyloidosis. European Journal of Neurology. 32(11). e70374–e70374.
3.
Lillo, Rosa E., et al.. (2024). Call center data modeling: a queueing science approach based on Markovian arrival process. Quality Technology & Quantitative Management. 22(4). 631–658. 6 indexed citations
4.
Srivastava, Ajitesh, et al.. (2024). Nowcasting Temporal Trends Using Indirect Surveys. Proceedings of the AAAI Conference on Artificial Intelligence. 38(20). 22359–22367. 1 indexed citations
5.
Luigetti, Marco, Ângela Romano, Valeria Guglielmino, et al.. (2024). Emerging multisystem biomarkers in hereditary transthyretin amyloidosis: a pilot study. Scientific Reports. 14(1). 18281–18281. 2 indexed citations
6.
Meucci, Maria Chiara, Rosa E. Lillo, Antonella Lombardo, et al.. (2024). Right Ventricular to Pulmonary Artery Coupling and Prognosis in Transthyretin Cardiac Amyloidosis. Journal of the American Society of Echocardiography. 37(12). 1188–1190.e3. 4 indexed citations
7.
Lillo, Rosa E., Francesca Graziani, Gabriella Locorotondo, et al.. (2024). Pulmonary Congestion Assessed by Lung Ultrasound in Patients with Severe Aortic Stenosis Undergoing Transcatheter Aortic Valve Implantation: Prevalence and Prognostic Implications. European Journal of Heart Failure. 26(10). 2107–2117. 5 indexed citations
8.
Cifuentes, Jenny, et al.. (2023). NN2Poly: A Polynomial Representation for Deep Feed-Forward Artificial Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 36(1). 781–795. 4 indexed citations
9.
Lillo, Rosa E., et al.. (2023). A fast epigraph and hypograph-based approach for clustering functional data. Statistics and Computing. 33(2). 3 indexed citations
10.
Aguilar, José, Carlos Baquero, Jaya Prakash Champati, et al.. (2023). Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection. Heliyon. 10(1). e23219–e23219. 3 indexed citations
11.
Aguilar, José, Carlos Baquero, Jaya Prakash Champati, et al.. (2023). Consistent comparison of symptom-based methods for COVID-19 infection detection. International Journal of Medical Informatics. 177. 105133–105133. 5 indexed citations
12.
Baquero, Carlos, Davide Frey, Christin Glorioso, et al.. (2023). Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection. Scientific Reports. 13(1). 900–900. 4 indexed citations
13.
Bernardino, Éléna Di, et al.. (2021). On the estimation of extreme directional multivariate quantiles. El Repositorio Institucional de la Universidad EAFIT (Universidad EAFIT). 2 indexed citations
14.
Cifuentes, Jenny, et al.. (2021). Towards a mathematical framework to inform neural network modelling via polynomial regression. Neural Networks. 142. 57–72. 31 indexed citations
15.
Buño, Ismael, et al.. (2020). Variable selection with P‐splines in functional linear regression: Application in graft‐versus‐host disease. Biometrical Journal. 62(7). 1670–1686. 2 indexed citations
16.
Lillo, Rosa E., et al.. (2014). Failure modeling of an electrical N-component framework by the non-stationary Markovian arrival process. Reliability Engineering & System Safety. 134. 126–133. 14 indexed citations
17.
Torrado, Nuria & Rosa E. Lillo. (2012). Likelihood ratio order of spacings from two heterogeneous samples. Journal of Multivariate Analysis. 114. 338–348. 10 indexed citations
18.
Lillo, Rosa E., et al.. (2011). Comparing quantile residual life functions by confidence bands. Lifetime Data Analysis. 18(2). 195–214. 7 indexed citations
19.
Ramírez‐Cobo, Pepa & Rosa E. Lillo. (2011). New Results About Weakly Equivalent MAP 2 and MAP 3 Processes. Methodology And Computing In Applied Probability. 14(3). 421–444. 4 indexed citations
20.
García‐Sánchez, F., et al.. (2002). Two new HLA class I alleles recognised by PCR sequence‐specific primer and sequencing based typing: B*3805 and Cw*0408. Tissue Antigens. 59(1). 47–48. 4 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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