Maria L. Rizzo
- Statistics and Probability top 0.5%
- Artificial Intelligence top 2%
- Molecular Biology
- Economics and Econometrics top 5%
- Finance top 5%
- Co-authors
- Gábor J. SzékelyMargarita UdallPaul B. RobbinsJim DohertyE.C. FaulknerKimberly Andrews EspyJim AlbertHua Fang
- Topics
- Statistical Methods and Inference (10 papers)Advanced Statistical Methods and Models (8 papers)Statistical Distribution Estimation and Applications (6 papers)
- Partner nations
- United StatesHungaryTürkiye
In The Last Decade
Maria L. Rizzo
30 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 196
- Statistics and Probability 782
- Artificial Intelligence 678
- Molecular Biology 395
- Economics and Econometrics 204
- Finance 198
Countries citing papers authored by Maria L. Rizzo
This map shows the geographic impact of Maria L. Rizzo'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 Maria L. Rizzo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria L. Rizzo more than expected).
Fields of papers citing papers by Maria L. Rizzo
This network shows the impact of papers produced by Maria L. Rizzo. 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 Maria L. Rizzo. The network helps show where Maria L. Rizzo may publish in the future.
Co-authorship network of co-authors of Maria L. Rizzo
This figure shows the co-authorship network connecting the top 25 collaborators of Maria L. Rizzo. A scholar is included among the top collaborators of Maria L. Rizzo 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 Maria L. Rizzo. Maria L. Rizzo 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 | E-Statistics: Multivariate Inference via the Energy of Data [R package energy version 1.7-7] | 4 |
| 3 | 144 | |
| 4 | 103 | |
| 5 | 14 | |
| 6 | 183 | |
| 7 | Energy statistics: A class of statistics based on distancesbreakdown → | 344 |
| 8 | 7 | |
| 9 | 12 | |
| 10 | 85 | |
| 11 | 4 | |
| 12 | 25 | |
| 13 | Brownian distance covariancebreakdown → | 515 |
| 14 | 40 | |
| 15 | 21 | |
| 16 | 39 | |
| 17 | 5 | |
| 18 | 40 | |
| 19 | Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Methodbreakdown → | 452 |
| 20 | 203 |
About Maria L. Rizzo
Maria L. Rizzo is a scholar working on Statistics and Probability, Finance and Analytical Chemistry, having authored 30 papers that have together received 2.7k indexed citations. Recurring topics across this work include Statistical Methods and Inference (10 papers), Advanced Statistical Methods and Models (8 papers) and Statistical Distribution Estimation and Applications (6 papers). The work is most often cited by research in Statistics and Probability (782 citations), Statistics, Probability and Uncertainty (162 citations) and Artificial Intelligence (678 citations). Maria L. Rizzo has collaborated with scholars based in United States, Hungary and Türkiye. Frequent co-authors include Gábor J. Székely, Margarita Udall, Paul B. Robbins, Jim Doherty, E.C. Faulkner, Kimberly Andrews Espy, Jim Albert, Hua Fang, Zhenyuan Wang and Honggang Wang. Their work appears in journals such as Pattern Recognition, American Mathematical Monthly and PharmacoEconomics.
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.