Pavlo Mozharovskyi
- Statistics and Probability top 2%
- Advanced Statistical Methods and Models 12
- Statistical Methods and Inference 4
- Aging top 10%
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- Advanced Statistical Process Monitoring 7
- Health Informatics top 10%
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- Spatial and Panel Data Analysis 3
- Economic and Environmental Valuation 2
- Economic Growth and Productivity 2
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- Anomaly Detection Techniques and Applications 3
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- Neural dynamics and brain function 2
Pavlo Mozharovskyi
22 papers receiving 353 citations
Peers
Comparison fields: 5 of 108
- Statistics and Probability 147
- Aging 24
- Statistics, Probability and Uncertainty 91
- Health Informatics 16
- Management Science and Operations Research 70
Countries citing papers authored by Pavlo Mozharovskyi
This map shows the geographic impact of Pavlo Mozharovskyi'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 Pavlo Mozharovskyi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pavlo Mozharovskyi more than expected).
Fields of papers citing papers by Pavlo Mozharovskyi
This network shows the impact of papers produced by Pavlo Mozharovskyi. 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 Pavlo Mozharovskyi. The network helps show where Pavlo Mozharovskyi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pavlo Mozharovskyi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 2 | |
| 4 | 2023 | 7 | |
| 5 | 2022 | 18 | |
| 6 | 2022 | 7 | |
| 7 | 2021 | 32 | |
| 8 | Depth-Based Classification and Calculation of Data Depth [R package ddalpha version 1.3.11] | 2020 | 1 |
| 9 | 2020 | 7 | |
| 10 | 2020 | 36 | |
| 11 | 2019 | 12 | |
| 12 | 2019 | 6 | |
| 13 | Mesh Generation and Surface Tessellation [R package geometry version 0.4.5] | 2019 | 9 |
| 14 | 2018 | 16 | |
| 15 | 2016 | 4 | |
| 16 | 2016 | 55 | |
| 17 | 2016 | 0 | |
| 18 | 2015 | 16 | |
| 19 | 2015 | 39 | |
| 20 | 2014 | 10 |
About Pavlo Mozharovskyi
Pavlo Mozharovskyi is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty, Aging, Management Science and Operations Research and Artificial Intelligence, having authored 25 papers that have together received 372 indexed citations. Recurring topics across this work include Advanced Statistical Methods and Models (12 papers), Advanced Statistical Process Monitoring (7 papers), Statistical Methods and Inference (4 papers), Spatial and Panel Data Analysis (3 papers), Anomaly Detection Techniques and Applications (3 papers), Economic and Environmental Valuation (2 papers), Neural dynamics and brain function (2 papers) and Economic Growth and Productivity (2 papers). The work is most often cited by research in Statistics and Probability (147 citations), Aging (24 citations), Statistics, Probability and Uncertainty (91 citations), Health Informatics (16 citations) and Management Science and Operations Research (70 citations). Pavlo Mozharovskyi has collaborated with scholars based in France, Germany and Belgium. Frequent co-authors include Karl Mosler, Oleg Badunenko, Rainer Dyckerhoff, Florence d’Alché–Buc, Stéphan Clémençon, Xiaohui Liu, James Eagan, David Bounie, Winston Maxwell and Isabelle Bloch. Their work appears in journals such as Journal of the American Statistical Association, Journal of Computational and Graphical Statistics, Statistical Papers, Computational Statistics & Data Analysis and Advances in Data Analysis and Classification.
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.