Seminar for Statistics
Impact in
- Statistics and Probability top 5%
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
- Advanced Statistical Methods and Models
-
- Ecology and Vegetation Dynamics Studies
Papers in
-
- Statistical Methods and Inference 139
- Statistical Methods and Bayesian Inference 63
- Advanced Statistical Methods and Models 46
- Advanced Causal Inference Techniques 34
-
- Bayesian Methods and Mixture Models 46
- Bayesian Modeling and Causal Inference 42
- Top scholars
- Peter BühlmannDaniel Johannes StekhovenMartin MächlerAnders NielsenJames S. HansKasper KristensenÁrni MagnússonSara van de Geer
- Journals
- Electronic Journal of Statistics (18 papers)Biometrika (16 papers)The Annals of Statistics (12 papers)Journal of Statistical Planning and Inference (11 papers)Computational Statistics & Data Analysis (10 papers)
- Partner nations
- SwitzerlandGermanyUnited States
In The Last Decade
Seminar for Statistics
399 papers receiving 32.8k citations
Peers
Comparison fields: 5 of 233
- Statistics and Probability 4.0k
- Nature and Landscape Conservation 2.3k
- Ecology, Evolution, Behavior and Systematics 3.4k
- Ecological Modeling 753
- Developmental Biology 374
Countries citing scholars working at Seminar for Statistics
This map shows the geographic impact of research produced by authors working at Seminar for Statistics. 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 papers produced at Seminar for Statistics with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seminar for Statistics more than expected).
Fields of papers published by authors at Seminar for Statistics
This network shows the impact of papers affiliated with Seminar for Statistics at the time of their publication. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers affiliated with Seminar for Statistics at the time of their publication.
About Seminar for Statistics
In recent decades, authors affiliated with Seminar for Statistics have published 476 papers, which have received a total of 41.7k indexed citations . Scholars at this organization have produced 191 papers in Statistics and Probability, 118 papers in Artificial Intelligence, 18 papers in Statistics, Probability and Uncertainty, 21 papers in Finance and 40 papers in Atmospheric Science on the topics of Statistical Methods and Inference (139 papers), Statistical Methods and Bayesian Inference (63 papers), Advanced Statistical Methods and Models (46 papers), Bayesian Methods and Mixture Models (46 papers), Bayesian Modeling and Causal Inference (42 papers), Advanced Causal Inference Techniques (34 papers), Meteorological Phenomena and Simulations (27 papers) and Climate variability and models (25 papers). Their work is cited by papers focused on Statistics and Probability (4.0k citations), Nature and Landscape Conservation (2.3k citations), Ecology, Evolution, Behavior and Systematics (3.4k citations), Ecological Modeling (753 citations) and Developmental Biology (374 citations). Authors at Seminar for Statistics collaborate with scholars in Switzerland, Germany and United States and have published in prestigious journals including Electronic Journal of Statistics, Biometrika, The Annals of Statistics, Journal of Statistical Planning and Inference and Computational Statistics & Data Analysis. Some of Seminar for Statistics's most productive authors include Peter Bühlmann, Daniel Johannes Stekhoven, Martin Mächler, Anders Nielsen, James S. Hans, Kasper Kristensen, Árni Magnússon, Sara van de Geer, Marcel Dettling and Hans Rudolf Künsch.
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.