Graciela Boente
- Statistics and Probability top 0.5%
- Statistics, Probability and Uncertainty top 0.5%
- Artificial Intelligence top 5%
- Analytical Chemistry top 5%
- Finance top 5%
- Co-authors
- Ricardo FraimanAna M. BiancoDaniela RodríguezMatías Salibián‐BarreraDavid E. TylerJane-Ling WangWenceslao González–ManteigaDouglas G. Simpson
- Topics
- Advanced Statistical Methods and Models (70 papers)Statistical Methods and Inference (70 papers)Advanced Statistical Process Monitoring (36 papers)
In The Last Decade
Graciela Boente
85 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 107
- Statistics and Probability 964
- Statistics, Probability and Uncertainty 308
- Artificial Intelligence 218
- Analytical Chemistry 104
- Finance 90
Countries citing papers authored by Graciela Boente
This map shows the geographic impact of Graciela Boente'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 Graciela Boente with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Graciela Boente more than expected).
Fields of papers citing papers by Graciela Boente
This network shows the impact of papers produced by Graciela Boente. 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 Graciela Boente. The network helps show where Graciela Boente may publish in the future.
Co-authorship network of co-authors of Graciela Boente
This figure shows the co-authorship network connecting the top 25 collaborators of Graciela Boente. A scholar is included among the top collaborators of Graciela Boente 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 Graciela Boente. Graciela Boente is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 4 | |
| 7 | Robust smoothed canonical correlation analysis for functional data | 2 |
| 8 | Robust estimators in a generalized partly linear regression model under monotony constraints | 3 |
| 9 | 11 | |
| 10 | 17 | |
| 11 | A test for the equality of covariance operators | 1 |
| 12 | 3 | |
| 13 | 21 | |
| 14 | 13 | |
| 15 | 15 | |
| 16 | 5 | |
| 17 | Asymptotic distribution and strong order of convergence of robust non parametric regression estimates | 1 |
| 18 | 41 | |
| 19 | 9 | |
| 20 | Qualitative Robustness for General Stochastic Processes. | 4 |
About Graciela Boente
Graciela Boente is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Finance, having authored 86 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Statistical Methods and Models (70 papers), Statistical Methods and Inference (70 papers) and Advanced Statistical Process Monitoring (36 papers). The work is most often cited by research in Statistics and Probability (964 citations), Statistics, Probability and Uncertainty (308 citations) and Analytical Chemistry (104 citations). Graciela Boente has collaborated with scholars based in Argentina, Spain and Portugal. Frequent co-authors include Ricardo Fraiman, Ana M. Bianco, Daniela Rodríguez, Matías Salibián‐Barrera, David E. Tyler, Jane-Ling Wang, Wenceslao González–Manteiga, Douglas G. Simpson, Babette Brumback and Mariano J. Valderrama. Their work appears in journals such as Journal of the American Statistical Association, Technometrics and Biometrika.
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.