Andrew J. Holbrook

775 total citations
21 papers, 290 citations indexed

About

Andrew J. Holbrook is a scholar working on Artificial Intelligence, Genetics and Statistics and Probability. According to data from OpenAlex, Andrew J. Holbrook has authored 21 papers receiving a total of 290 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 7 papers in Genetics and 7 papers in Statistics and Probability. Recurrent topics in Andrew J. Holbrook's work include Bayesian Methods and Mixture Models (6 papers), Markov Chains and Monte Carlo Methods (5 papers) and Dementia and Cognitive Impairment Research (4 papers). Andrew J. Holbrook is often cited by papers focused on Bayesian Methods and Mixture Models (6 papers), Markov Chains and Monte Carlo Methods (5 papers) and Dementia and Cognitive Impairment Research (4 papers). Andrew J. Holbrook collaborates with scholars based in United States, Belgium and United Kingdom. Andrew J. Holbrook's co-authors include Daniel L. Gillen, Michael A. Yassa, Nicholas J. Tustison, Philip A. Cook, Brian Avants, James R. Stone, Jeffrey Duda, James C. Gee, Gabriel A. Devenyi and Nicholas Cullen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Methods and Scientific Reports.

In The Last Decade

Andrew J. Holbrook

20 papers receiving 285 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Andrew J. Holbrook United States 8 86 76 45 39 39 21 290
Elvan Ceyhan Türkiye 12 70 0.8× 100 1.3× 36 0.8× 51 1.3× 10 0.3× 47 405
Charles Shang United States 13 41 0.5× 21 0.3× 138 3.1× 18 0.5× 62 1.6× 22 423
Arndt Wilcke Germany 14 28 0.3× 157 2.1× 79 1.8× 26 0.7× 144 3.7× 21 537
Suraj Muley United States 13 220 2.6× 335 4.4× 54 1.2× 17 0.4× 15 0.4× 24 685
Matthieu Perrot France 9 155 1.8× 221 2.9× 21 0.5× 33 0.8× 5 0.1× 17 370
Kate Devlin United Kingdom 10 28 0.3× 107 1.4× 30 0.7× 25 0.6× 6 0.2× 22 720
Casey Goodlett United States 9 285 3.3× 78 1.0× 14 0.3× 21 0.5× 16 0.4× 16 371
Willem de Winter Netherlands 9 14 0.2× 35 0.5× 91 2.0× 5 0.1× 32 0.8× 17 441
Aiying Zhang United States 12 102 1.2× 203 2.7× 27 0.6× 17 0.4× 14 0.4× 31 344
Arent de Jongh Netherlands 11 127 1.5× 394 5.2× 17 0.4× 92 2.4× 51 1.3× 15 570

Countries citing papers authored by Andrew J. Holbrook

Since Specialization
Citations

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

Fields of papers citing papers by Andrew J. Holbrook

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew J. Holbrook. 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 Andrew J. Holbrook. The network helps show where Andrew J. Holbrook may publish in the future.

Co-authorship network of co-authors of Andrew J. Holbrook

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew J. Holbrook. A scholar is included among the top collaborators of Andrew J. Holbrook 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 Andrew J. Holbrook. Andrew J. Holbrook is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Baele, Guy, Xiang Ji, Gabriel W. Hassler, et al.. (2025). BEAST X for Bayesian phylogenetic, phylogeographic and phylodynamic inference. Nature Methods. 22(8). 1653–1656. 8 indexed citations
2.
Glatt-Holtz, Nathan, et al.. (2024). On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2. Proceedings of the National Academy of Sciences. 121(3). e2318989121–e2318989121. 3 indexed citations
3.
Magee, Andrew F., Andrew J. Holbrook, Jonathan E. Pekar, et al.. (2024). Random-Effects Substitution Models for Phylogenetics via Scalable Gradient Approximations. Systematic Biology. 73(3). 562–578. 5 indexed citations
4.
Glatt-Holtz, Nathan, et al.. (2024). Parallel MCMC algorithms: theoretical foundations, algorithm design, case studies. PubMed. 8(2). 3 indexed citations
5.
Tustison, Nicholas J., Michael A. Yassa, Batool Rizvi, et al.. (2024). ANTsX neuroimaging-derived structural phenotypes of UK Biobank. Scientific Reports. 14(1). 8848–8848. 1 indexed citations
6.
Nishimura, Akihiko, Nídia S. Trovão, Joshua L. Cherry, et al.. (2023). Accelerating Bayesian inference of dependency between mixed-type biological traits. PLoS Computational Biology. 19(8). e1011419–e1011419. 4 indexed citations
7.
Holbrook, Andrew J.. (2023). A Quantum Parallel Markov Chain Monte Carlo. Journal of Computational and Graphical Statistics. 32(4). 1402–1415. 3 indexed citations
8.
Hassler, Gabriel W., Brigida Gallone, Leandro Arístide, et al.. (2022). Principled, practical, flexible, fast: A new approach to phylogenetic factor analysis. Methods in Ecology and Evolution. 13(10). 2181–2197. 16 indexed citations
9.
Holbrook, Andrew J.. (2022). Generating MCMC proposals by randomly rotating the regular simplex. Journal of Multivariate Analysis. 194. 105106–105106. 4 indexed citations
10.
Holbrook, Andrew J., Xiang Ji, & Marc A. Suchard. (2022). Bayesian mitigation of spatial coarsening for a Hawkes model applied to gunfire, wildfire and viral contagion. The Annals of Applied Statistics. 16(1). 573–595. 5 indexed citations
11.
Tustison, Nicholas J., Philip A. Cook, Andrew J. Holbrook, et al.. (2021). The ANTsX ecosystem for quantitative biological and medical imaging. Scientific Reports. 11(1). 9068–9068. 135 indexed citations
12.
Ji, Xiang, Zhenyu Zhang, Andrew J. Holbrook, et al.. (2020). Gradients Do Grow on Trees: A Linear-TimeO(N)-Dimensional Gradient for Statistical Phylogenetics. Molecular Biology and Evolution. 37(10). 3047–3060. 17 indexed citations
13.
Holbrook, Andrew J., Shiwei Lan, Jeffrey Streets, & Babak Shahbaba. (2020). Nonparametric fisher geometry with application to density estimation. 101–110. 2 indexed citations
14.
Lan, Shiwei, et al.. (2019). Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices. Bayesian Analysis. 15(4). 1199–1228. 5 indexed citations
15.
Tustison, Nicholas J., Andrew J. Holbrook, Brian Avants, et al.. (2019). Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer’s Disease Neuroimaging Initiative-Based Evaluation Study. Journal of Alzheimer s Disease. 71(1). 165–183. 31 indexed citations
16.
Holbrook, Andrew J., et al.. (2017). Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation. Journal of Statistical Computation and Simulation. 88(5). 982–1002. 11 indexed citations
17.
Holbrook, Andrew J., et al.. (2017). A Bayesian supervised dual‐dimensionality reduction model for simultaneous decoding of LFP and spike train signals. Stat. 6(1). 53–67. 6 indexed citations
18.
Lan, Shiwei, et al.. (2017). Flexible Bayesian Dynamic Modeling of Covariance and Correlation Matrices - eScholarship. 1 indexed citations
19.
Grill, Joshua D., et al.. (2017). Attitudes toward Potential Participant Registries. Journal of Alzheimer s Disease. 56(3). 939–946. 8 indexed citations
20.
Lan, Shiwei, Andrew J. Holbrook, Norbert J. Fortin, Hernando Ombao, & Babak Shahbaba. (2017). Flexible Bayesian Dynamic Modeling of Covariance and Correlation Matrices.

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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