William S. Cleveland

40.5k total citations · 10 hit papers
141 papers, 28.0k citations indexed

About

William S. Cleveland is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistics and Probability. According to data from OpenAlex, William S. Cleveland has authored 141 papers receiving a total of 28.0k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 32 papers in Computer Vision and Pattern Recognition and 23 papers in Statistics and Probability. Recurrent topics in William S. Cleveland's work include Data Visualization and Analytics (30 papers), Advanced Statistical Methods and Models (17 papers) and Data Analysis with R (13 papers). William S. Cleveland is often cited by papers focused on Data Visualization and Analytics (30 papers), Advanced Statistical Methods and Models (17 papers) and Data Analysis with R (13 papers). William S. Cleveland collaborates with scholars based in United States, Canada and Australia. William S. Cleveland's co-authors include Susan J. Devlin, Robert McGill, John M. Chambers, Paul A. Tukey, Brian M. Kleiner, Richard A. Becker, M. J. R. Healy, N. I. Fisher, Eric H. Grosse and Beat Kleiner and has published in prestigious journals such as Science, SHILAP Revista de lepidopterología and Journal of Geophysical Research Atmospheres.

In The Last Decade

William S. Cleveland

138 papers receiving 25.4k citations

Hit Papers

Robust Locally Weighted R... 1979 2026 1994 2010 1979 1988 1984 1984 1979 2.5k 5.0k 7.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Robust Locally Weighted Regression and Smoothing Scatterplots
    1979 8.1k citations William S. Cleveland Journal of the American Statistical Association profile →
  2. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
    1988 4.1k citations William S. Cleveland, Susan J. Devlin Journal of the American Statistical Association profile →
  3. Graphical Methods for Data Analysis.
    1984 1.8k citations William S. Cleveland et al. profile →
  4. Graphical Methods for Data Analysis.
    1984 1.4k citations N. I. Fisher, William S. Cleveland et al. profile →
  5. Robust Locally Weighted Regression and Smoothing Scatterplots
    1979 1.4k citations William S. Cleveland Journal of the American Statistical Association profile →
  6. Visualizing Data
    1993 1.2k citations William S. Cleveland profile →
  7. The elements of graphing data
    1985 1.0k citations William S. Cleveland profile →
  8. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods
    1984 987 citations William S. Cleveland, Robert McGill Journal of the American Statistical Association profile →
  9. LOWESS: A Program for Smoothing Scatterplots by Robust Locally Weighted Regression
    1981 880 citations William S. Cleveland The American Statistician profile →
  10. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
    1988 579 citations William S. Cleveland, Susan J. Devlin Journal of the American Statistical Association profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
William S. Cleveland United States 46 4.2k 3.5k 3.0k 2.8k 2.1k 141 28.0k
Ian T. Jolliffe United Kingdom 39 3.9k 0.9× 4.4k 1.3× 3.6k 1.2× 1.6k 0.6× 1.8k 0.9× 115 29.4k
Brian P. Flannery United States 33 4.1k 1.0× 3.7k 1.1× 2.5k 0.8× 941 0.3× 1.1k 0.5× 79 49.1k
Saul A. Teukolsky United States 66 4.4k 1.0× 4.3k 1.2× 2.8k 0.9× 1.1k 0.4× 1.1k 0.5× 233 74.6k
William H. Press United States 60 4.4k 1.0× 4.5k 1.3× 3.2k 1.1× 1.2k 0.4× 1.3k 0.6× 183 74.4k
William T. Vetterling United States 24 4.0k 1.0× 3.7k 1.1× 2.3k 0.8× 937 0.3× 941 0.4× 62 47.5k
John W. Tukey United States 70 4.7k 1.1× 6.7k 1.9× 2.5k 0.8× 8.1k 2.9× 2.8k 1.3× 229 52.7k
Richard A. Olshen United States 41 3.4k 0.8× 12.0k 3.4× 2.8k 0.9× 2.5k 0.9× 1.4k 0.7× 119 43.3k
J. A. Hartigan United States 35 3.3k 0.8× 6.9k 2.0× 1.3k 0.4× 2.1k 0.8× 897 0.4× 83 22.2k
B. D. Ripley United Kingdom 48 2.8k 0.7× 5.8k 1.6× 6.2k 2.1× 3.5k 1.2× 2.6k 1.3× 168 50.8k
Kurt Hornik Austria 61 3.1k 0.7× 11.9k 3.4× 2.1k 0.7× 1.5k 0.5× 2.0k 0.9× 269 39.7k

Countries citing papers authored by William S. Cleveland

Since Specialization
Citations

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

Fields of papers citing papers by William S. Cleveland

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William S. Cleveland. 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 William S. Cleveland. The network helps show where William S. Cleveland may publish in the future.

Co-authorship network of co-authors of William S. Cleveland

This figure shows the co-authorship network connecting the top 25 collaborators of William S. Cleveland. A scholar is included among the top collaborators of William S. Cleveland 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 William S. Cleveland. William S. Cleveland 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.
Ruggeri, Fabrizio, David Banks, William S. Cleveland, et al.. (2025). Is There a Future for Stochastic Modeling in Business and Industry in the Era of Machine Learning and Artificial Intelligence?. Applied Stochastic Models in Business and Industry. 41(2).
2.
Zhang, Chen, Wen‐wen Tung, & William S. Cleveland. (2021). In Search of the Optimal Atmospheric River Index for US Precipitation: A Multifactorial Analysis. Journal of Geophysical Research Atmospheres. 126(10). 4 indexed citations
3.
Tung, Wen‐wen, et al.. (2018). Divide and recombine (D&R) data science projects for deep analysis of big data and high computational complexity. Japanese Journal of Statistics and Data Science. 1(1). 139–156. 5 indexed citations
4.
Hafen, Ryan, et al.. (2012). Large complex data: divide and recombine (D&R) with RHIPE. Stat. 1(1). 53–67. 64 indexed citations
5.
Cleveland, William S., et al.. (2010). Between Grace and Fear: The Role of the Arts in a Time of Change. 4 indexed citations
6.
Hafen, Ryan, et al.. (2009). Visualization Databases for the Analysis of Large Complex Datasets. International Conference on Artificial Intelligence and Statistics. 193–200. 14 indexed citations
7.
Maciejewski, Ross, Yun Jang, Cheng Zheng, et al.. (2007). LAHVA: Linked Animal-Human Health Visual Analytics. Purdue e-Pubs (Purdue University System). 27–34. 23 indexed citations
8.
Cleveland, William S., Lorraine Denby, & Chuanhai Liu. (2000). Random Location and Scale Effects: Model Building Methods for a General Class of Models. Brain and Language. 223. 105029–105029. 7 indexed citations
9.
Cleveland, William S. & Don X. Sun. (2000). Internet Traffic Data. Journal of the American Statistical Association. 95(451). 979–979. 3 indexed citations
10.
Becker, Richard A. & William S. Cleveland. (1993). Discussion of “Graphic Comparisons of Several Linked Aspects” by John W. Tukey. Journal of Computational and Graphical Statistics. 2(1). 41–48. 1 indexed citations
11.
Cleveland, William S., et al.. (1993). ATS Methods: Nonparametric Regression for Non-Gaussian Data. Journal of the American Statistical Association. 88(423). 821–835. 11 indexed citations
12.
Cleveland, William S., et al.. (1988). The Shape Parameter of a Two-Variable Graph. Journal of the American Statistical Association. 83(402). 289–300. 58 indexed citations
13.
Cleveland, William S.. (1987). Research in Statistical Graphics. Journal of the American Statistical Association. 82(398). 419–423. 37 indexed citations
14.
Becker, Richard A. & William S. Cleveland. (1987). Brushing Scatterplots. Technometrics. 29(2). 127–142. 299 indexed citations
15.
Cleveland, William S. & Robert McGill. (1986). An experiment in graphical perception. International Journal of Man-Machine Studies. 25(5). 491–500. 106 indexed citations
16.
Cleveland, William S. & Robert McGill. (1984). The Many Faces of a Scatterplot. Journal of the American Statistical Association. 79(388). 807–822. 144 indexed citations
17.
Cleveland, William S.. (1984). Graphs in Scientific Publications. The American Statistician. 38(4). 261–269. 111 indexed citations
18.
Cleveland, William S. & Susan J. Devlin. (1982). Calendar Effects in Monthly Time Series: Modeling and Adjustment. Journal of the American Statistical Association. 77(379). 520–528. 38 indexed citations
19.
Cleveland, William S.. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association. 74(368). 829–836. 8055 indexed citations breakdown →
20.
Cleveland, William S., et al.. (1978). SABL: A Resistant Seasonal Adjustment Procedure With Graphical Methods for Interpretation and Diagnosis. NBER Chapters. 201–241. 30 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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