James M. Landwehr

991 citations
22 papers · 716 indexed · h-index 9
Topics
Advanced Clustering Algorithms Research (4 papers)Optimal Experimental Design Methods (3 papers)Manufacturing Process and Optimization (2 papers)
Partner nations
United StatesCanadaItaly

In The Last Decade

James M. Landwehr

22 papers receiving 651 citations

Peers

James M. Landwehr
Comparison fields: 5 of 135
  • Statistics and Probability 236
  • Artificial Intelligence 179
  • Computer Vision and Pattern Recognition 138
  • Signal Processing 60
  • Ecology 51
Replace Ryuei Nishii with:
Ryuei Nishii Japan
Jeffrey Glosup United States
Shean‐Tsong Chiu United States
Silviu Guiaşu Canada
Howell Tong United Kingdom
Brett Presnell United States
Christophe Giraud France
John T. Ormerod Australia
Mariano J. Valderrama Spain
Barbara A. Moore United States
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Citations per field
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Ryuei Nishii · 1×
Citations per year

Countries citing papers authored by James M. Landwehr

Since Specialization
Citations

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

Fields of papers citing papers by James M. Landwehr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James M. Landwehr. 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 James M. Landwehr. The network helps show where James M. Landwehr may publish in the future.

Co-authorship network of co-authors of James M. Landwehr

This figure shows the co-authorship network connecting the top 25 collaborators of James M. Landwehr. A scholar is included among the top collaborators of James M. Landwehr 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 James M. Landwehr. James M. Landwehr 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
#WorkIndexed citations
1
3
2 12
3
Tukey's Paper After 40 Years, With Discussion
4
4 4
5 7
6 1
7 8
8 5
9 7
10 27
11 3
12 21
13 1
14 1
15 82
16 5
17 198
18 154
19 146
20 5

About James M. Landwehr

James M. Landwehr is a scholar working on Statistics and Probability, Management Science and Operations Research and Industrial and Manufacturing Engineering, having authored 22 papers that have together received 716 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (4 papers), Optimal Experimental Design Methods (3 papers) and Manufacturing Process and Optimization (2 papers). The work is most often cited by research in Statistics and Probability (236 citations), Computer Vision and Pattern Recognition (138 citations) and Signal Processing (60 citations). James M. Landwehr has collaborated with scholars based in United States, Canada and Italy. Frequent co-authors include Daryl Pregibon, Joseph B. Kruskal, Anne C. Shoemaker, Michel Jambu, Anne E. Freeny, Jure Zupan, Edward B. Fowlkes, Henry M. Wilbur, Douglas M. Dunn and J. D. Sinclair. Their work appears in journals such as Journal of the American Statistical Association, Ecology and Technometrics.

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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