Paul McCullagh

174 papers receiving 5.9k citations

Paul McCullagh's Hit Papers

Generalized Linear Models 1992 · 2.2k citations
2.2k0+14+28Years since publication50010001.5k2.0k

Peers

Paul McCullagh
Comparison fields: 5 of 215
  • Statistics and Probability 709
  • Nature and Landscape Conservation 612
  • Ecological Modeling 184
  • Computer Vision and Pattern Recognition 739
  • Human-Computer Interaction 168
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Countries citing papers authored by Paul McCullagh

Since Specialization
Citations

This map shows the geographic impact of Paul McCullagh'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 Paul McCullagh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul McCullagh more than expected).

Fields of papers citing papers by Paul McCullagh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paul McCullagh. 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 Paul McCullagh. The network helps show where Paul McCullagh may publish in the future.

Co-authors

The 25 scholars most cited alongside Paul McCullagh, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Paul McCullagh Line = papers co-authored together Paul McCullagh links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 182 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Generalized Linear Models
Hit paper breakdown →
19922151
2
Generalized Linear Models
Hit paper breakdown →
19831571
3 2007120
4 2007104
5 2020102
6 201799
7 201676
8 201468
9 201068
10 201066
11 200665
12 201561
13 198255
14 200955
15 200854
16 201845
17 201042
18 200740
19 201039
20 197738

About Paul McCullagh

Paul McCullagh is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience, Artificial Intelligence, General Health Professions and Surgery, having authored 182 papers that have together received 6.4k indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (36 papers), EEG and Brain-Computer Interfaces (25 papers), Mobile Health and mHealth Applications (15 papers), Technology Use by Older Adults (12 papers), Gaze Tracking and Assistive Technology (8 papers), Neuroscience and Neural Engineering (8 papers), Non-Invasive Vital Sign Monitoring (7 papers) and ECG Monitoring and Analysis (7 papers). The work is most often cited by research in Statistics and Probability (709 citations), Nature and Landscape Conservation (612 citations), Ecological Modeling (184 citations), Computer Vision and Pattern Recognition (739 citations) and Human-Computer Interaction (168 citations). Paul McCullagh has collaborated with scholars based in United Kingdom, Australia and Ireland. Frequent co-authors include J. A. Nelder, John H. Schuenemeyer, Juan Carlos Augusto, Chris Nugent, N.D. Black, Huiru Zheng, Maurice Mulvenna, Shumei Zhang, Yue Huang and Roy Harper. Their work appears in journals such as Journal of Comparative Pathology, Scandinavian Journal of Immunology, IEEE Transactions on Human-Machine Systems, Journal of Reproductive Immunology and Artificial Intelligence in Medicine.

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.

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