Paul D. McNicholas
- Artificial Intelligence top 0.5%
- Statistics and Probability top 0.5%
- Molecular Biology
- Physiology top 5%
- Signal Processing top 5%
- Co-authors
- Thomas Brendan MurphyRyan P. BrowneJeffrey L. AndrewsSanjeena SubediStuart M. PhillipsBrian C. FranczakLehana ThabaneAlexandra Mayhew
- Topics
- Bayesian Methods and Mixture Models (68 papers)Advanced Clustering Algorithms Research (36 papers)Statistical Methods and Bayesian Inference (24 papers)
- Partner nations
- CanadaUnited StatesItaly
In The Last Decade
Paul D. McNicholas
102 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 182
- Artificial Intelligence 1.7k
- Statistics and Probability 984
- Molecular Biology 451
- Physiology 383
- Signal Processing 215
Countries citing papers authored by Paul D. McNicholas
This map shows the geographic impact of Paul D. McNicholas'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 D. McNicholas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul D. McNicholas more than expected).
Fields of papers citing papers by Paul D. McNicholas
This network shows the impact of papers produced by Paul D. McNicholas. 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 D. McNicholas. The network helps show where Paul D. McNicholas may publish in the future.
Co-authorship network of co-authors of Paul D. McNicholas
This figure shows the co-authorship network connecting the top 25 collaborators of Paul D. McNicholas. A scholar is included among the top collaborators of Paul D. McNicholas based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Paul D. McNicholas. Paul D. McNicholas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 5 | |
| 8 | 16 | |
| 9 | 9 | |
| 10 | 5 | |
| 11 | 17 | |
| 12 | 48 | |
| 13 | Flexible clustering of high-dimensional data via mixtures of joint generalized hyperbolic distributions: Mixtures of joint generalized hyperbolic distributions | 3 |
| 14 | 16 | |
| 15 | 10 | |
| 16 | 3 | |
| 17 | 26 | |
| 18 | Mixtures of Multiple Scaled Generalized Hyperbolic Distributions | 2 |
| 19 | Robust Clustering via Parsimonious Mixtures of Contaminated Gaussian Distributions | 2 |
| 20 | On Model-Based Clustering, Classification, and Discriminant Analysis | 7 |
About Paul D. McNicholas
Paul D. McNicholas is a scholar working on Statistics and Probability, Artificial Intelligence and Geriatrics and Gerontology, having authored 110 papers that have together received 3.1k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (68 papers), Advanced Clustering Algorithms Research (36 papers) and Statistical Methods and Bayesian Inference (24 papers). The work is most often cited by research in Statistics and Probability (984 citations), Artificial Intelligence (1.7k citations) and Computational Mathematics (21 citations). Paul D. McNicholas has collaborated with scholars based in Canada, United States and Italy. Frequent co-authors include Thomas Brendan Murphy, Ryan P. Browne, Jeffrey L. Andrews, Sanjeena Subedi, Stuart M. Phillips, Brian C. Franczak, Lehana Thabane, Alexandra Mayhew, Parminder Raina and Gianni Parise. Their work appears in journals such as Bioinformatics, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.