Local Regression and Likelihood
- Authors
- Clive Loader
- Journal
- CERN Document Server (European Organization for Nuclear Research)
In The Last Decade
doi.org/10.1007/b98858 →Countries where authors are citing Local Regression and Likelihood
This map shows the geographic impact of Local Regression and 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 Local Regression and Likelihood with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Local Regression and Likelihood more than expected).
Fields of papers citing Local Regression and Likelihood
This network shows the impact of Local Regression and Likelihood. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Local Regression and Likelihood.
About Local Regression and Likelihood
This paper, published in 1999, received 835 indexed citations . Written by Clive Loader covering the research area of Applied Mathematics, Statistics and Probability and Artificial Intelligence. It is primarily cited by scholars working on Statistics and Probability (176 citations), Economics and Econometrics (127 citations), Global and Planetary Change (114 citations), Artificial Intelligence (108 citations) and Computer Vision and Pattern Recognition (75 citations). Published in CERN Document Server (European Organization for Nuclear 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.
This paper is also available at doi.org/10.1007/b98858.