Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Extreme Value Theory in Engineering.
1989654 citationsEnrique Castillo et al.Journal of the American Statistical Associationprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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Countries citing papers authored by Enrique Castillo
Since
Specialization
Citations
This map shows the geographic impact of Enrique Castillo'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 Enrique Castillo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Enrique Castillo more than expected).
Fields of papers citing papers by Enrique Castillo
This network shows the impact of papers produced by Enrique Castillo. 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 Enrique Castillo. The network helps show where Enrique Castillo may publish in the future.
Co-authorship network of co-authors of Enrique Castillo
This figure shows the co-authorship network connecting the top 25 collaborators of Enrique Castillo.
A scholar is included among the top collaborators of Enrique Castillo 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 Enrique Castillo. Enrique Castillo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Castillo, Enrique. (2013). On the Qualities of a Leading Journal. Computer-Aided Civil and Infrastructure Engineering. 28(9). 649–650.1 indexed citations
4.
Fernández‐Canteli, Alfonso, et al.. (2013). 160 Towards a probabilistic concept of the Kitagawa-Takahashi diagram. Gruppo Italiano Frattura Digital Repository (Gruppo Italiano Frattura).2 indexed citations
5.
Castillo, Enrique, et al.. (2008). Introduction. Computer-Aided Civil and Infrastructure Engineering. 23(2). 75–75.
6.
Castillo, Enrique, Bertha Guijarro‐Berdiñas, Óscar Fontenla-Romero, & Amparo Alonso‐Betanzos. (2006). A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis. Journal of Machine Learning Research. 7(42). 1159–1182.85 indexed citations
7.
Sánchez‐Maroño, Noelia, Amparo Alonso‐Betanzos, & Enrique Castillo. (2005). a new wrapper method for feature subset selection.. The European Symposium on Artificial Neural Networks. 515–520.8 indexed citations
8.
Castillo, Enrique. (2005). Extreme value and related models with applications in engineering and science. Wiley eBooks.238 indexed citations
9.
Sarabia, José Marı́a & Enrique Castillo. (2005). About a class of max-stable families with applications to income distributions. METRON. 505–527.14 indexed citations
Fontenla-Romero, Óscar, Deniz Erdoğmuş, José C. Prı́ncipe, Amparo Alonso‐Betanzos, & Enrique Castillo. (2003). Accelerating the convergence speed of neural networks learning methods using least squares.. The European Symposium on Artificial Neural Networks. 255–260.11 indexed citations
Castillo, Enrique, José Manuel Gutiérrez, & Ali S. Hadi. (1998). Improving Search-Based Inference in Bayesian Networks. The Florida AI Research Society. 405–409.
16.
Castillo, Enrique, José María Gutiérrez Martínez, & Ali S. Hadi. (1996). Expert Systems and Probabiistic Network Models. Springer eBooks.95 indexed citations
17.
Castillo, Enrique, José Manuel Gutiérrez, & Ali S. Hadi. (1996). Goal oriented symbolic propagation in Bayesian networks. National Conference on Artificial Intelligence. 1263–1268.9 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.