Inferential statistical methods for estimating and comparing cosinor parameters.
Impact in
- Physiology 141
- Journal
- PubMed
In The Last Decade
doi.org/w49393611 →Countries where authors are citing Inferential statistical methods for estimating and comparing cosinor parameters.
This map shows the geographic impact of Inferential statistical methods for estimating and comparing cosinor parameters.. 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 Inferential statistical methods for estimating and comparing cosinor parameters. with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Inferential statistical methods for estimating and comparing cosinor parameters. more than expected).
Fields of papers citing Inferential statistical methods for estimating and comparing cosinor parameters.
This network shows the impact of Inferential statistical methods for estimating and comparing cosinor parameters.. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Inferential statistical methods for estimating and comparing cosinor parameters..
About Inferential statistical methods for estimating and comparing cosinor parameters.
This paper, published in 1983, received 512 indexed citations . Written by C. Raymond Bingham, B. Arbogast and F. Halberg. It is primarily cited by scholars working on Endocrine and Autonomic Systems (241 citations), Physiology (141 citations), Cardiology and Cardiovascular Medicine (98 citations), Cellular and Molecular Neuroscience (47 citations) and Cognitive Neuroscience (37 citations). Published in PubMed.
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/w49393611.