Tracy Holsclaw

615 total citations
9 papers, 419 citations indexed

About

Tracy Holsclaw is a scholar working on Artificial Intelligence, Astronomy and Astrophysics and Global and Planetary Change. According to data from OpenAlex, Tracy Holsclaw has authored 9 papers receiving a total of 419 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Astronomy and Astrophysics and 3 papers in Global and Planetary Change. Recurrent topics in Tracy Holsclaw's work include Gaussian Processes and Bayesian Inference (3 papers), Cosmology and Gravitation Theories (3 papers) and Hydrology and Drought Analysis (3 papers). Tracy Holsclaw is often cited by papers focused on Gaussian Processes and Bayesian Inference (3 papers), Cosmology and Gravitation Theories (3 papers) and Hydrology and Drought Analysis (3 papers). Tracy Holsclaw collaborates with scholars based in United States, Australia and Norway. Tracy Holsclaw's co-authors include Salman Habib, David Higdon, Katrin Heitmann, Ujjaini Alam, Bruno Sansó, Herbert Lee, Padhraic Smyth, Arthur M. Greene, Andrew W. Robertson and Herbert K. H. Lee and has published in prestigious journals such as Physical Review Letters, Technometrics and Journal of Hydrometeorology.

In The Last Decade

Tracy Holsclaw

8 papers receiving 408 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tracy Holsclaw United States	7	304	107	48	40	27	9	419
I. J. O’Dwyer United States	11	359 1.2×	109 1.0×	45 0.9×	15 0.4×	36 1.3×	18	444
Jeffrey Jewell United States	8	249 0.8×	76 0.7×	34 0.7×	43 1.1×	20 0.7×	13	326
Jeffrey B. Jewell United States	7	293 1.0×	105 1.0×	21 0.4×	9 0.2×	28 1.0×	15	326
Benjamin L’Huillier South Korea	15	481 1.6×	193 1.8×	21 0.4×	17 0.4×	40 1.5×	27	506
M. Gaug Spain	9	99 0.3×	158 1.5×	47 1.0×	31 0.8×	16 0.6×	45	254
D. Rubin United States	9	252 0.8×	87 0.8×	13 0.3×	9 0.2×	12 0.4×	31	333
R. Stompor France	12	341 1.1×	125 1.2×	8 0.2×	13 0.3×	14 0.5×	33	394
S Anselmi Italy	11	206 0.7×	79 0.7×	12 0.3×	41 1.0×	24 0.9×	20	282
Moritz Münchmeyer United States	11	355 1.2×	192 1.8×	9 0.2×	8 0.2×	20 0.7×	19	384
Greg Huey United States	11	522 1.7×	367 3.4×	18 0.4×	8 0.2×	59 2.2×	14	541

Countries citing papers authored by Tracy Holsclaw

Since Specialization

Citations

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

Fields of papers citing papers by Tracy Holsclaw

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tracy Holsclaw

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

All Works

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9 of 9 papers shown

Cao, Qing, Hanchen Zhang, Upmanu Lall, Tracy Holsclaw, & Quanxi Shao. (2023). The predictability of daily rainfall during rainy season over East Asia by a Bayesian nonhomogeneous hidden Markov model. Journal of Flood Risk Management. 17(1).

Holsclaw, Tracy, Arthur M. Greene, Andrew W. Robertson, & Padhraic Smyth. (2017). Bayesian nonhomogeneous Markov models via Pólya-Gamma data augmentation with applications to rainfall modeling. The Annals of Applied Statistics. 11(1). 37 indexed citations

Holsclaw, Tracy, Arthur M. Greene, Andrew W. Robertson, & Padhraic Smyth. (2015). A Bayesian Hidden Markov Model of Daily Precipitation over South and East Asia. Journal of Hydrometeorology. 17(1). 3–25. 27 indexed citations

Holsclaw, Tracy, et al.. (2015). Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences. Technometrics. 58(2). 205–213. 5 indexed citations

Holsclaw, Tracy, Kevin A. Hallgren, Mark Steyvers, Padhraic Smyth, & David C. Atkins. (2015). Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.. Psychology of Addictive Behaviors. 29(4). 1031–1040. 12 indexed citations

Holsclaw, Tracy, Bruno Sansó, Herbert K. H. Lee, et al.. (2012). Gaussian Process Modeling of Derivative Curves. Technometrics. 55(1). 57–67. 29 indexed citations

Holsclaw, Tracy, Ujjaini Alam, Bruno Sansó, et al.. (2011). Nonparametric reconstruction of the dark energy equation of state from diverse data sets. Physical review. D. Particles, fields, gravitation, and cosmology. 84(8). 70 indexed citations

Holsclaw, Tracy, Ujjaini Alam, Bruno Sansó, et al.. (2010). Nonparametric Dark Energy Reconstruction from Supernova Data. Physical Review Letters. 105(24). 241302–241302. 140 indexed citations

Holsclaw, Tracy, Ujjaini Alam, Bruno Sansó, et al.. (2010). Nonparametric reconstruction of the dark energy equation of state. Physical review. D. Particles, fields, gravitation, and cosmology. 82(10). 99 indexed citations

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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