Olive Jean Dunn

16.2k total citations · 5 hit papers
65 papers, 12.0k citations indexed

About

Olive Jean Dunn is a scholar working on Statistics and Probability, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Olive Jean Dunn has authored 65 papers receiving a total of 12.0k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Statistics and Probability, 7 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Olive Jean Dunn's work include Advanced Statistical Methods and Models (28 papers), Statistical Methods and Inference (9 papers) and Statistical Methods and Bayesian Inference (5 papers). Olive Jean Dunn is often cited by papers focused on Advanced Statistical Methods and Models (28 papers), Statistical Methods and Inference (9 papers) and Statistical Methods and Bayesian Inference (5 papers). Olive Jean Dunn collaborates with scholars based in United States. Olive Jean Dunn's co-authors include Virginia A. Clark, Madan L. Puri, Pranab Kumar Sen, Peter B. Imrey, N. M. Downie, Leonard M. Linde, Jane Galbraith, Linda S. Chan, Peter Sprent and Max H. Myers and has published in prestigious journals such as Journal of the American Statistical Association, Technometrics and PEDIATRICS.

In The Last Decade

Olive Jean Dunn

64 papers receiving 11.1k citations

Hit Papers

Multiple Comparisons among Means 1961 2026 1982 2004 1961 1964 1972 1976 1961 1000 2.0k 3.0k

Peers

Olive Jean Dunn
Comparison fields: 5 of 235
  • Statistics and Probability 1.5k
  • Artificial Intelligence 1.2k
  • Molecular Biology 1.1k
  • Ecology 966
  • Cognitive Neuroscience 689
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Citations per field, relative to Olive Jean Dunn
Olive Jean Dunn · 1×
Citations per year, relative to Olive Jean Dunn
Olive Jean Dunn · 1×

Countries citing papers authored by Olive Jean Dunn

Since Specialization
Citations

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

Fields of papers citing papers by Olive Jean Dunn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Olive Jean Dunn. 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 Olive Jean Dunn. The network helps show where Olive Jean Dunn may publish in the future.

Co-authorship network of co-authors of Olive Jean Dunn

This figure shows the co-authorship network connecting the top 25 collaborators of Olive Jean Dunn. A scholar is included among the top collaborators of Olive Jean Dunn 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 Olive Jean Dunn. Olive Jean Dunn 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
# Work Indexed citations
1 9
2
Applied statistics: analysis of variance and regression (2nd ed.)
53
3 5
4 38
5
Applied Statistics: Analysis of Variance and Regression. breakdown →
496
6 174
7 15
8 151
9 130
10 10
11 228
12 65
13 20
14 20
15 52
16 69
17
Multiple Comparisons Using Rank Sums breakdown →
3303
18
Multiple Comparisons among Means breakdown →
3333
19
Multiple Comparisons Among Means breakdown →
376
20 153

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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