William Jay Conover
Impact in
- Statistics, Probability and Uncertainty top 0.02%
- Probabilistic and Robust Engineering Design
- Statistics and Probability top 0.1%
- Probability and Statistical Research
- Advanced Statistical Methods and Models
Papers in
-
- Probability and Statistical Research 48
- Statistical Distribution Estimation and Applications 13
- Advanced Statistical Methods and Models 13
- Statistical Methods and Applications 8
- Statistics Education and Methodologies 7
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- Advanced Statistical Process Monitoring 7
- Co-authors
- Michael D. McKay (4 shared papers)Richard J. Beckman (3 shared papers)Ronald L. Iman (22 shared papers)David J. Groggel (1 shared paper)Bruce M. Brown (1 shared paper)Richard A. Groeneveld (1 shared paper)John Haigh (1 shared paper)Clint W. Coakley (1 shared paper)
- Journals
- Journal of the American Statistical Association (15 papers)Technometrics (11 papers)Biometrics (3 papers)Journal of Statistical Computation and Simulation (3 papers)The American Statistician (3 papers)
- Partner nations
- United StatesMexicoSpain
In The Last Decade
William Jay Conover
71 papers receiving 35k citations
William Jay Conover's Hit Papers
Peers
Comparison fields: 5 of 237
- Statistics, Probability and Uncertainty 3.1k
- Statistics and Probability 2.3k
- Management Science and Operations Research 2.7k
- Computational Theory and Mathematics 2.1k
- Environmental Engineering 1k
Countries citing papers authored by William Jay Conover
This map shows the geographic impact of William Jay Conover'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 Jay Conover with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Jay Conover more than expected).
Fields of papers citing papers by William Jay Conover
This network shows the impact of papers produced by William Jay Conover. 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 Jay Conover. The network helps show where William Jay Conover may publish in the future.
Co-authors
The 25 scholars most cited alongside William Jay Conover, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 75 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Practical Nonparametric Statistics Hit paper breakdown → | 2000 | 6290 |
| 2 | A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code Hit paper breakdown → | 2000 | 5824 |
| 3 | Rank Transformations as a Bridge Between Parametric and Nonparametric Statistics Hit paper breakdown → | 1981 | 3280 |
| 4 | A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code Hit paper breakdown → | 1979 | 3173 |
| 5 | Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code Hit paper breakdown → | 1979 | 2571 |
| 6 | Practical Non-Parametric Statistics. Hit paper breakdown → | 1981 | 2249 |
| 7 | A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code Hit paper breakdown → | 2000 | 2098 |
| 8 | Practical Nonparametric Statistics (2nd ed). Hit paper breakdown → | 1981 | 1642 |
| 9 | 1981 | 1376 | |
| 10 | A distribution-free approach to inducing rank correlation among input variables Hit paper breakdown → | 1982 | 1227 |
| 11 | Practical Nonparametric Statistics Hit paper breakdown → | 2000 | 1122 |
| 12 | 1981 | 1006 | |
| 13 | 1972 | 815 | |
| 14 | 1974 | 772 | |
| 15 | 1981 | 743 | |
| 16 | 1980 | 649 | |
| 17 | 1979 | 413 | |
| 18 | 1982 | 369 | |
| 19 | 1983 | 233 | |
| 20 | 1976 | 168 |
About William Jay Conover
William Jay Conover is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty, Management Science and Operations Research, Ecology and Management of Technology and Innovation, having authored 75 papers that have together received 37.6k indexed citations. Recurring topics across this work include Probability and Statistical Research (48 papers), Statistical Distribution Estimation and Applications (13 papers), Advanced Statistical Methods and Models (13 papers), Statistical Methods and Applications (8 papers), Optimal Experimental Design Methods (8 papers), Statistics Education and Methodologies (7 papers), Advanced Statistical Process Monitoring (7 papers) and Simulation Techniques and Applications (5 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (3.1k citations), Statistics and Probability (2.3k citations), Management Science and Operations Research (2.7k citations), Computational Theory and Mathematics (2.1k citations) and Environmental Engineering (1k citations). William Jay Conover has collaborated with scholars based in United States, Mexico and Spain. Frequent co-authors include Michael D. McKay, Richard J. Beckman, Ronald L. Iman, David J. Groggel, Bruce M. Brown, Richard A. Groeneveld, John Haigh, Clint W. Coakley, Thomas E. Obremski and Pranab Kumar Sen. Their work appears in journals such as Journal of the American Statistical Association, Technometrics, Biometrics, Journal of Statistical Computation and Simulation and The American Statistician.
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.