Paul D. McNicholas

5.7k citations
110 papers · 3.1k indexed · 1 hit paper · h-index 32

Paul D. McNicholas

102 papers receiving 3.0k citations

Hit Papers

The prevalence of sarcopenia in community-dwelling older ...3232018202620202023100200300

Peers

Paul D. McNicholas
Comparison fields: 5 of 182
  • Statistics and Probability 984
  • Artificial Intelligence 1.7k
  • Computational Mathematics 21
  • Geriatrics and Gerontology 137
  • Signal Processing 215
Replace Jeng‐Min Chiou with:
Jeng‐Min Chiou Taiwan
Michael Cantor United States
Peihua Qiu United States
Xuming He United States
Pat Brown United States
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Paul D. McNicholas relative to Jeng‐Min Chiou Taiwan Jeng‐Min Chiou's profile →
Citations per field
00.5×3.7×
Jeng‐Min Chiou · 1×
Citations per year

Countries citing papers authored by Paul D. McNicholas

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

The 25 scholars most cited alongside Paul D. McNicholas, 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 D. McNicholas Line = papers co-authored together Paul D. McNicholas links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20241
3 20240
4 20233
5 20231
6 20230
7 20235
8 202116
9 20219
10 20185
11 201817
12 201848
13
Flexible clustering of high-dimensional data via mixtures of joint generalized hyperbolic distributions: Mixtures of joint generalized hyperbolic distributions
20183
14 201716
15 201710
16 20163
17 201526
18
Mixtures of Multiple Scaled Generalized Hyperbolic Distributions
20142
19
Robust Clustering via Parsimonious Mixtures of Contaminated Gaussian Distributions
20132
20
On Model-Based Clustering, Classification, and Discriminant Analysis
20117

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), Statistical Methods and Bayesian Inference (24 papers), Gene expression and cancer classification (15 papers), Statistical Methods and Inference (13 papers), Statistical Distribution Estimation and Applications (8 papers), Advanced Statistical Methods and Models (7 papers) and Sensory Analysis and Statistical Methods (6 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.

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