Dan J. Spitzner
- Statistics, Probability and Uncertainty top 0.5%
- Statistics and Probability top 2%
- Control and Systems Engineering top 10%
- Artificial Intelligence
- Industrial and Manufacturing Engineering top 10%
- Co-authors
- William H. WoodallShilpa GuptaDouglas C. MontgomeryYun-Tae KimRichard L. SmithMontserrat FuentesGreg K. EssickJ. S. Marron
- Topics
- Statistical Methods and Bayesian Inference (5 papers)Statistical Methods and Inference (5 papers)Advanced Statistical Methods and Models (5 papers)
- Cited by
- Statistics, Probability and UncertaintyStatistics and ProbabilityMedical Laboratory Technology
- Journals
- Journal of the American Statistical AssociationStatistics in MedicineJournal of the Royal Statistical Society Series B (Statistical Methodology)
- Partner nations
- United States
In The Last Decade
Dan J. Spitzner
16 papers receiving 508 citations
Peers
Comparison fields: 5 of 91
- Statistics, Probability and Uncertainty 358
- Statistics and Probability 241
- Control and Systems Engineering 133
- Artificial Intelligence 50
- Industrial and Manufacturing Engineering 46
Countries citing papers authored by Dan J. Spitzner
This map shows the geographic impact of Dan J. Spitzner'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 Dan J. Spitzner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan J. Spitzner more than expected).
Fields of papers citing papers by Dan J. Spitzner
This network shows the impact of papers produced by Dan J. Spitzner. 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 Dan J. Spitzner. The network helps show where Dan J. Spitzner may publish in the future.
Co-authorship network of co-authors of Dan J. Spitzner
This figure shows the co-authorship network connecting the top 25 collaborators of Dan J. Spitzner. A scholar is included among the top collaborators of Dan J. Spitzner 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 Dan J. Spitzner. Dan J. Spitzner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 3 | |
| 5 | 23 | |
| 6 | 2 | |
| 7 | Testing in functional data analysis using quadratic forms | 1 |
| 8 | 6 | |
| 9 | 2 | |
| 10 | 6 | |
| 11 | 15 | |
| 12 | 4 | |
| 13 | 3 | |
| 14 | 387 | |
| 15 | 26 | |
| 16 | 4 | |
| 17 | 40 |
About Dan J. Spitzner
Dan J. Spitzner is a scholar working on Statistics and Probability, General Social Sciences and Statistics, Probability and Uncertainty, having authored 17 papers that have together received 529 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (5 papers), Statistical Methods and Inference (5 papers) and Advanced Statistical Methods and Models (5 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (358 citations), Statistics and Probability (241 citations) and Medical Laboratory Technology (13 citations). Dan J. Spitzner has collaborated with scholars based in United States. Frequent co-authors include William H. Woodall, Shilpa Gupta, Douglas C. Montgomery, Yun-Tae Kim, Richard L. Smith, Montserrat Fuentes, Greg K. Essick, J. S. Marron, J. Brooke Marshall and Ronald D. Fricker. Their work appears in journals such as Journal of the American Statistical Association, Statistics in Medicine and Journal of the Royal Statistical Society Series B (Statistical Methodology).
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.