In all likelihood : statistical modelling and inference using likelihood
Impact in
- Authors
- Yudi Pawitan
- Journal
- Oxford University Press eBooks
In The Last Decade
doi.org/w2861562 →Countries where authors are citing In all likelihood : statistical modelling and inference using likelihood
This map shows the geographic impact of In all likelihood : statistical modelling and inference using likelihood. 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 In all likelihood : statistical modelling and inference using likelihood with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites In all likelihood : statistical modelling and inference using likelihood more than expected).
Fields of papers citing In all likelihood : statistical modelling and inference using likelihood
This network shows the impact of In all likelihood : statistical modelling and inference using likelihood. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the In all likelihood : statistical modelling and inference using likelihood.
About In all likelihood : statistical modelling and inference using likelihood
This paper, published in 2001, received 717 indexed citations . Written by Yudi Pawitan. It is primarily cited by scholars working on Statistics and Probability (177 citations), Artificial Intelligence (91 citations), Statistics, Probability and Uncertainty (85 citations), Global and Planetary Change (67 citations) and Management Science and Operations Research (65 citations). Published in Oxford University Press eBooks.
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.
This paper is also available at doi.org/w2861562.