Marta Pérez‐Casany
- Statistics and Probability top 5%
- Artificial Intelligence
- Ecology
- Nature and Landscape Conservation
- Global and Planetary Change
- Co-authors
- Joan del CastilloEva CastellsPedro PuigJordi ValeroJosep GinebraJosep-L. Larriba-PeyDavid Domínguez-SalXavier Puig
- Topics
- Bayesian Methods and Mixture Models (6 papers)Statistical Distribution Estimation and Applications (4 papers)Statistical Methods and Bayesian Inference (3 papers)
In The Last Decade
Marta Pérez‐Casany
15 papers receiving 258 citations
Peers
Comparison fields: 5 of 90
- Statistics and Probability 119
- Artificial Intelligence 66
- Ecology 41
- Nature and Landscape Conservation 40
- Global and Planetary Change 28
Countries citing papers authored by Marta Pérez‐Casany
This map shows the geographic impact of Marta Pérez‐Casany'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 Marta Pérez‐Casany with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marta Pérez‐Casany more than expected).
Fields of papers citing papers by Marta Pérez‐Casany
This network shows the impact of papers produced by Marta Pérez‐Casany. 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 Marta Pérez‐Casany. The network helps show where Marta Pérez‐Casany may publish in the future.
Co-authorship network of co-authors of Marta Pérez‐Casany
This figure shows the co-authorship network connecting the top 25 collaborators of Marta Pérez‐Casany. A scholar is included among the top collaborators of Marta Pérez‐Casany 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 Marta Pérez‐Casany. Marta Pérez‐Casany is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 1 | |
| 3 | Zipf Extended Distributions [R package zipfextR version 1.0.2] | 1 |
| 4 | 3 | |
| 5 | 104 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 4 | |
| 11 | 8 | |
| 12 | 8 | |
| 13 | 7 | |
| 14 | 0 | |
| 15 | 68 | |
| 16 | 54 |
About Marta Pérez‐Casany
Marta Pérez‐Casany is a scholar working on Statistics and Probability, Mathematical Physics and Statistics, Probability and Uncertainty, having authored 16 papers that have together received 267 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (6 papers), Statistical Distribution Estimation and Applications (4 papers) and Statistical Methods and Bayesian Inference (3 papers). The work is most often cited by research in Statistics and Probability (119 citations), Ecological Modeling (25 citations) and Statistics, Probability and Uncertainty (28 citations). Marta Pérez‐Casany has collaborated with scholars based in Spain, Denmark and Germany. Frequent co-authors include Joan del Castillo, Eva Castells, Pedro Puig, Jordi Valero, Josep Ginebra, Josep-L. Larriba-Pey, David Domínguez-Sal, Xavier Puig, Axel Thieffry and Eftim Zdravevski. Their work appears in journals such as Physica A Statistical Mechanics and its Applications, Methods in Ecology and Evolution and Computational Statistics & Data Analysis.
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.