Neyko Neykov
Impact in
- Statistics and Probability top 2%
- Advanced Statistical Methods and Models
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
- Statistical Distribution Estimation and Applications
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- Advanced Statistical Process Monitoring
Papers in
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- Advanced Statistical Methods and Models 9
- Statistical Methods and Bayesian Inference 3
- Statistical Methods and Inference 3
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- Advanced Statistical Process Monitoring 4
- Co-authors
- Plamen Neytchev (10 shared papers)Peter Filzmoser (5 shared papers)Rositsa B. Dimova (2 shared papers)Christine H. Müller (1 shared paper)Pieter van Gelder (2 shared papers)Valentin Todorov (2 shared papers)John de Ronde (1 shared paper)Walter Zucchini (2 shared papers)
In The Last Decade
Neyko Neykov
13 papers receiving 295 citations
Peers
Comparison fields: 5 of 57
- Statistics and Probability 186
- Statistics, Probability and Uncertainty 62
- Artificial Intelligence 99
- Global and Planetary Change 51
- Analytical Chemistry 19
Countries citing papers authored by Neyko Neykov
This map shows the geographic impact of Neyko Neykov'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 Neyko Neykov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neyko Neykov more than expected).
Fields of papers citing papers by Neyko Neykov
This network shows the impact of papers produced by Neyko Neykov. 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 Neyko Neykov. The network helps show where Neyko Neykov may publish in the future.
Co-authors
The 11 scholars most cited alongside Neyko Neykov, 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 | 2006 | 115 | |
| 2 | 2003 | 38 | |
| 3 | 1998 | 32 | |
| 4 | 2001 | 27 | |
| 5 | 2007 | 20 | |
| 6 | 2011 | 15 | |
| 7 | 2013 | 12 | |
| 8 | 2014 | 11 | |
| 9 | 2011 | 11 | |
| 10 | 1994 | 11 | |
| 11 | 2009 | 9 | |
| 12 | 2011 | 8 | |
| 13 | MIXTURE OF GLMS AND THE TRIMMED LIKELIHOOD METHODOLOGY | 2004 | 3 |
| 14 | 2025 | 0 |
About Neyko Neykov
Neyko Neykov is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty, Atmospheric Science, Global and Planetary Change and Artificial Intelligence, having authored 14 papers that have together received 312 indexed citations. Recurring topics across this work include Advanced Statistical Methods and Models (9 papers), Advanced Statistical Process Monitoring (4 papers), Hydrology and Drought Analysis (3 papers), Climate variability and models (3 papers), Statistical Methods and Bayesian Inference (3 papers), Statistical Methods and Inference (3 papers), Spectroscopy and Chemometric Analyses (2 papers) and Meteorological Phenomena and Simulations (2 papers). The work is most often cited by research in Statistics and Probability (186 citations), Statistics, Probability and Uncertainty (62 citations), Artificial Intelligence (99 citations), Global and Planetary Change (51 citations) and Analytical Chemistry (19 citations). Neyko Neykov has collaborated with scholars based in Bulgaria, Austria and Germany. Frequent co-authors include Plamen Neytchev, Peter Filzmoser, Rositsa B. Dimova, Christine H. Müller, Pieter van Gelder, Valentin Todorov, John de Ronde, Walter Zucchini, Pavel Čı́žek and Milka Todorova. Their work appears in journals such as Computational Statistics & Data Analysis, Journal of Statistical Planning and Inference, Environmental and Ecological Statistics, Natural Product Communications and Water Resources Research.
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.