Paul D. McNicholas

5.7k total citations · 1 hit paper
110 papers, 3.1k citations indexed

About

Paul D. McNicholas is a scholar working on Artificial Intelligence, Statistics and Probability and Molecular Biology. According to data from OpenAlex, Paul D. McNicholas has authored 110 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Artificial Intelligence, 38 papers in Statistics and Probability and 21 papers in Molecular Biology. Recurrent topics in Paul D. McNicholas's work include Bayesian Methods and Mixture Models (68 papers), Advanced Clustering Algorithms Research (36 papers) and Statistical Methods and Bayesian Inference (24 papers). Paul D. McNicholas is often cited by papers focused on Bayesian Methods and Mixture Models (68 papers), Advanced Clustering Algorithms Research (36 papers) and Statistical Methods and Bayesian Inference (24 papers). Paul D. McNicholas collaborates with scholars based in Canada, United States and Italy. Paul D. McNicholas's 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 and has published in prestigious journals such as Bioinformatics, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Paul D. McNicholas

102 papers receiving 3.0k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The prevalence of sarcopenia in community-dwelling older adults, an exploration of differences between studies and within definitions: a systematic review and meta-analyses

2018 323 citations Paul D. McNicholas et al. profile →

Peers

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 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

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20 of 20 papers shown

#	Work	Indexed citations
1	Balanced longitudinal data clustering with a copula kernel mixture model 2025 ·Canadian Journal of Statistics ·(unknown), (unknown), Paul D. McNicholas	0
2	Finding Outliers in Gaussian Model-based Clustering 2024 ·Journal of Classification ·(unknown), Paul D. McNicholas	1
3	The association of physical activity duration and intensity on emotional intelligence in 10–13 year-old Children 2024 ·Sports Medicine and Health Science ·(unknown), Tongjian You, Richard K. Fleming, Paul D. McNicholas, Philimon Gona	0
4	Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data 2023 ·Bioinformatics ·(unknown), (unknown), Steven J. Rothstein, Paul D. McNicholas, Sanjeena Subedi	3
5	VLF: An R package for the analysis of very low frequency variants in DNA sequences 2023 ·ZooKeys ·(unknown), Taryn Athey, Paul D. McNicholas, Robert Hanner	1
6	Clustering and semi‐supervised classification for clickstream data via mixture models 2023 ·Canadian Journal of Statistics ·(unknown), Paul D. McNicholas	0
7	Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions 2023 ·Journal of Classification ·Utkarsh J. Dang, (unknown), Ryan P. Browne, Paul D. McNicholas	5
8	Mixtures of Matrix-Variate Contaminated Normal Distributions 2021 ·Journal of Computational and Graphical Statistics ·Salvatore D. Tomarchio, (unknown), Antonio Punzo, Paul D. McNicholas	16
9	Identification of five important genes to predict glioblastoma subtypes 2021 ·Neuro-Oncology Advances ·(unknown), Maleeha Qazi, Kevin R. Brown, Nicholas Mikolajewicz, Jason Moffat, Sheila K. Singh, Paul D. McNicholas	9
10	Flexible clustering of high‐dimensional data via mixtures of joint generalized hyperbolic distributions 2018 ·Stat ·(unknown), Ryan P. Browne, Paul D. McNicholas	5
11	Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data 2018 ·Computational Statistics & Data Analysis ·Yuhong Wei, (unknown), Paul D. McNicholas	17
12	Finite mixtures of skewed matrix variate distributions 2018 ·Pattern Recognition ·(unknown), Paul D. McNicholas	48
13	Flexible clustering of high-dimensional data via mixtures of joint generalized hyperbolic distributions: Mixtures of joint generalized hyperbolic distributions 2018 ·arXiv (Cornell University) ·(unknown), Ryan P. Browne, Paul D. McNicholas	3
14	Model‐based clustering for spatiotemporal data on air quality monitoring 2017 ·Environmetrics ·(unknown), Matthieu Marbac, Paul D. McNicholas	16
15	Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering 2017 ·Journal of Multivariate Analysis ·Paula Murray, Ryan P. Browne, Paul D. McNicholas	10
16	Handling missing data in consumer hedonic tests arising from direct scaling 2016 ·Journal of Sensory Studies ·Brian C. Franczak, John C. Castura, Ryan P. Browne, Christopher J. Findlay, Paul D. McNicholas	3
17	Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures 2015 ·Computational Statistics & Data Analysis ·(unknown), Paul D. McNicholas	26
18	Mixtures of Multiple Scaled Generalized Hyperbolic Distributions 2014 ·arXiv (Cornell University) ·Cristina Tortora, Brian C. Franczak, Ryan P. Browne, Paul D. McNicholas	2
19	Robust Clustering via Parsimonious Mixtures of Contaminated Gaussian Distributions 2013 ·arXiv (Cornell University) ·Antonio Punzo, Paul D. McNicholas	2
20	On Model-Based Clustering, Classification, and Discriminant Analysis 2011 ·Paul D. McNicholas	7

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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