V. Kůs
Impact in
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- Advanced Statistical Methods and Models
- Statistical Methods and Inference
Papers in
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- Statistical Methods and Inference 7
- Advanced Statistical Methods and Models 5
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- Bayesian Methods and Mixture Models 3
- Algorithms and Data Compression 3
- Computational Physics and Python Applications 2
- Co-authors
- Igor Vajda (2 shared papers)J. Franc (5 shared papers)Domingo Morales (2 shared papers)P. Bour (2 shared papers)Serge Dos Santos (4 shared papers)
- Journals
- Kybernetika (4 papers)The Journal of the Acoustical Society of America (1 paper)Metrika (2 papers)Communication in Statistics- Theory and Methods (1 paper)Journal of Physics Conference Series (7 papers)
In The Last Decade
V. Kůs
16 papers receiving 40 citations
Peers
Comparison fields: 5 of 32
- Statistics and Probability 16
- Statistics, Probability and Uncertainty 4
- Statistical and Nonlinear Physics 6
- Nuclear and High Energy Physics 6
- Artificial Intelligence 14
Countries citing papers authored by V. Kůs
This map shows the geographic impact of V. Kůs'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 V. Kůs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites V. Kůs more than expected).
Fields of papers citing papers by V. Kůs
This network shows the impact of papers produced by V. Kůs. 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 V. Kůs. The network helps show where V. Kůs may publish in the future.
Co-authors
The 5 scholars most cited alongside V. Kůs, 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 | EXTENSIONS OF THE PARAMETRIC FAMILIES OF DIVERGENCES USED IN STATISTICAL INFERENCE | 2008 | 8 |
| 2 | MODIFIED POWER DIVERGENCE ESTIMATORS IN NORMAL MODELS - SIMULATION AND COMPARATIVE STUDY | 2012 | 4 |
| 3 | 2017 | 4 | |
| 4 | Vliv polovodičových měničů na napájecí soustavu. | 2002 | 4 |
| 5 | 2016 | 4 | |
| 6 | 2016 | 4 | |
| 7 | 2004 | 4 | |
| 8 | 2013 | 3 | |
| 9 | 2015 | 3 | |
| 10 | 2012 | 3 | |
| 11 | Blended Φ-divergences with examples | 2003 | 1 |
| 12 | 2021 | 1 | |
| 13 | 2023 | 1 | |
| 14 | 2017 | 1 | |
| 15 | 2012 | 1 | |
| 16 | 2016 | 1 | |
| 17 | 2014 | 0 | |
| 18 | 2019 | 0 | |
| 19 | 2018 | 0 |
About V. Kůs
V. Kůs is a scholar working on Statistics and Probability, Artificial Intelligence, Statistical and Nonlinear Physics, Nuclear and High Energy Physics and Numerical Analysis, having authored 19 papers that have together received 47 indexed citations. Recurring topics across this work include Statistical Methods and Inference (7 papers), Advanced Statistical Methods and Models (5 papers), Statistical Mechanics and Entropy (4 papers), Particle physics theoretical and experimental studies (3 papers), Bayesian Methods and Mixture Models (3 papers), Algorithms and Data Compression (3 papers), Computational Physics and Python Applications (2 papers) and Mathematical Approximation and Integration (2 papers). The work is most often cited by research in Statistics and Probability (16 citations), Statistics, Probability and Uncertainty (4 citations), Statistical and Nonlinear Physics (6 citations), Nuclear and High Energy Physics (6 citations) and Artificial Intelligence (14 citations). V. Kůs has collaborated with scholars based in Czechia and France. Frequent co-authors include Igor Vajda, J. Franc, Domingo Morales, P. Bour and Serge Dos Santos. Their work appears in journals such as Kybernetika, The Journal of the Acoustical Society of America, Metrika, Communication in Statistics- Theory and Methods and Journal of Physics Conference Series.
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.