Lynne Billard

4.0k total citations
136 papers, 2.2k citations indexed

About

Lynne Billard is a scholar working on Statistics and Probability, Artificial Intelligence and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Lynne Billard has authored 136 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Statistics and Probability, 29 papers in Artificial Intelligence and 25 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Lynne Billard's work include Mathematical and Theoretical Epidemiology and Ecology Models (23 papers), COVID-19 epidemiological studies (22 papers) and Bayesian Methods and Mixture Models (14 papers). Lynne Billard is often cited by papers focused on Mathematical and Theoretical Epidemiology and Ecology Models (23 papers), COVID-19 epidemiological studies (22 papers) and Bayesian Methods and Mixture Models (14 papers). Lynne Billard collaborates with scholars based in United States, Australia and France. Lynne Billard's co-authors include Edwin Diday, Roy M. Anderson, David R. Cox, Graham F. Medley, Jennifer Le‐Rademacher, G.M. Pesti, Dmitry V. Vedenov, J.A. Cason, Jaejik Kim and Murray J. Vimy and has published in prestigious journals such as Nature, SHILAP Revista de lepidopterología and Journal of the American Statistical Association.

In The Last Decade

Lynne Billard

129 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lynne Billard United States 20 610 489 211 192 175 136 2.2k
H. V. Henderson New Zealand 24 195 0.3× 274 0.6× 157 0.7× 81 0.4× 159 0.9× 68 2.7k
Ib M. Skovgaard Denmark 27 279 0.5× 648 1.3× 92 0.4× 92 0.5× 131 0.7× 58 2.5k
Wesley O. Johnson United States 40 841 1.4× 1.9k 3.8× 194 0.9× 234 1.2× 356 2.0× 158 6.1k
Mohammad Reza Mahmoudi Iran 31 545 0.9× 260 0.5× 25 0.1× 239 1.2× 57 0.3× 149 2.5k
Wenjiang J. Fu United States 16 212 0.3× 517 1.1× 204 1.0× 145 0.8× 586 3.3× 29 3.5k
Dankmar Böhning United Kingdom 35 1.1k 1.8× 2.0k 4.1× 25 0.1× 361 1.9× 162 0.9× 187 4.0k
Gavin J. Gibson United Kingdom 37 727 1.2× 197 0.4× 19 0.1× 200 1.0× 206 1.2× 112 3.6k
Kenneth J. Koehler United States 31 365 0.6× 491 1.0× 405 1.9× 97 0.5× 57 0.3× 82 3.8k
Robert K. Tsutakawa United States 22 319 0.5× 1.1k 2.3× 31 0.1× 390 2.0× 87 0.5× 51 2.9k
Niels G. Becker Australia 35 301 0.5× 403 0.8× 58 0.3× 180 0.9× 875 5.0× 142 4.1k

Countries citing papers authored by Lynne Billard

Since Specialization
Citations

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

Fields of papers citing papers by Lynne Billard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lynne Billard

This figure shows the co-authorship network connecting the top 25 collaborators of Lynne Billard. A scholar is included among the top collaborators of Lynne Billard 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 Lynne Billard. Lynne Billard 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.
Zhu, J. & Lynne Billard. (2025). Clustering Interval-valued Data Using Principal Components. Journal of Statistical Theory and Practice. 19(4).
2.
Pesti, G.M. & Lynne Billard. (2025). How Percentage Yields and Other Scaled Data Should Be Analyzed and Presented. 1 indexed citations
3.
Billard, Lynne, et al.. (2023). MLE for the parameters of bivariate interval-valued model. Advances in Data Analysis and Classification. 18(4). 827–850. 4 indexed citations
4.
Pesti, G.M., et al.. (2023). Partition of variation for predicting experimental power with a broiler chicken example. Poultry Science. 102(7). 102698–102698. 1 indexed citations
5.
Nunes, Ricardo Vianna, et al.. (2018). Choosing sample sizes for various blood parameters of broiler chickens with normal and non-normal observations. Poultry Science. 97(10). 3746–3754. 25 indexed citations
6.
Roussot, Adrien, Lynne Billard, Jonathan Cottenet, et al.. (2013). Classification of hospital pathways in the management of cancer: Application to lung cancer in the region of burgundy. Cancer Epidemiology. 37(5). 688–696. 14 indexed citations
7.
Shim, M.Y., et al.. (2013). Effects of balanced dietary protein levels on egg production and egg quality parameters of individual commercial layers. Poultry Science. 92(10). 2687–2696. 49 indexed citations
8.
Billard, Lynne, et al.. (2013). A multi-stage compartmental model for HIV-infected individuals: I – Waiting time approach. Mathematical Biosciences. 249. 92–101. 12 indexed citations
9.
Billard, Lynne & Edwin Diday. (2011). Symbolic Data Analysis: Definition and Examples. Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD) (University Paris-Dauphine). 5 indexed citations
10.
Douzal-Chouakria, Ahlame, Lynne Billard, & Edwin Diday. (2011). Principal component analysis for interval‐valued observations. Statistical Analysis and Data Mining The ASA Data Science Journal. 4(2). 229–246. 50 indexed citations
11.
Le‐Rademacher, Jennifer & Lynne Billard. (2010). Likelihood functions and some maximum likelihood estimators for symbolic data. Journal of Statistical Planning and Inference. 141(4). 1593–1602. 33 indexed citations
12.
Billard, Lynne & Edwin Diday. (2007). Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics). John Wiley & Sons, Inc. eBooks. 62 indexed citations
13.
Billard, Lynne. (2001). Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, edited by H.-H. Bock and E. Diday.. Journal of Classification. 18. 291–294. 1 indexed citations
14.
Billard, Lynne, et al.. (1997). Computing science and statistics : graph-image-vision : proceedings of the 28th Symposium on the Interface, Sydney, Australia. 1 indexed citations
15.
Billard, Lynne. (1997). A Voyage of Discovery. Journal of the American Statistical Association. 92(437). 1–12. 4 indexed citations
16.
Billard, Lynne. (1994). Twenty Years Later: Is There Parity for Academic Women?.. Thought & action. 10(1). 11 indexed citations
17.
Billard, Lynne, et al.. (1993). A review and synthesis of the HIV/AIDS epidemic as a multi-stage process. Mathematical Biosciences. 117(1-2). 19–33. 6 indexed citations
18.
Medley, Graham F., Lynne Billard, David R. Cox, & Roy M. Anderson. (1988). The distribution of the incubation period for the acquired immunodeficiency syndrome (AIDS). Proceedings of the Royal Society of London. Series B, Biological sciences. 233(1272). 367–377. 53 indexed citations
19.
Billard, Lynne. (1985). Computer science and statistics : proceedings of the Sixteenth Symposium on the Interface, Atlanta, Georgia, March 1984. North-Holland eBooks. 3 indexed citations
20.
Billard, Lynne. (1974). Competition between two species. Stochastic Processes and their Applications. 2(4). 391–398. 12 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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