David Mease
Impact in
- Artificial Intelligence top 5%
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
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- Manufacturing Process and Optimization
Papers in
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- Probabilistic and Robust Engineering Design 3
- Advanced Statistical Process Monitoring 1
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- Advanced Statistical Methods and Models 2
- Co-authors
- Abraham J. Wyner (4 shared papers)Andreas Buja (1 shared paper)Vijayan N. Nair (3 shared papers)Justin Bleich (1 shared paper)Matthew Olson (1 shared paper)Agus Sudjianto (1 shared paper)Neema Moraveji (1 shared paper)Jacob Bien (1 shared paper)
- Journals
- Journal of Machine Learning Research (3 papers)Technometrics (2 papers)The American Statistician (2 papers)The Library Quarterly (1 paper)Statistica Sinica (1 paper)
- Partner nations
- United States
In The Last Decade
David Mease
15 papers receiving 680 citations
Peers
Comparison fields: 5 of 121
- Artificial Intelligence 322
- Industrial and Manufacturing Engineering 83
- Statistics and Probability 58
- Health Information Management 22
- Management of Technology and Innovation 37
Countries citing papers authored by David Mease
This map shows the geographic impact of David Mease'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 David Mease with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Mease more than expected).
Fields of papers citing papers by David Mease
This network shows the impact of papers produced by David Mease. 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 David Mease. The network helps show where David Mease may publish in the future.
Co-authors
The 14 scholars most cited alongside David Mease, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 200 | |
| 2 | 2017 | 154 | |
| 3 | Evidence Contrary to the Statistical View of Boosting | 2008 | 100 |
| 4 | 2004 | 87 | |
| 5 | 2003 | 45 | |
| 6 | 2011 | 32 | |
| 7 | 2009 | 31 | |
| 8 | 2014 | 24 | |
| 9 | Unique Optimal Partitions of Distributions and Connections to Hazard Rates and Stochastic Ordering | 2006 | 14 |
| 10 | 2006 | 7 | |
| 11 | 2013 | 7 | |
| 12 | Evidence Contrary to the Statistical View of Boosting: A Rejoinder to Responses | 2008 | 6 |
| 13 | 2003 | 3 | |
| 14 | 2011 | 1 | |
| 15 | 2004 | 1 |
About David Mease
David Mease is a scholar working on Statistics, Probability and Uncertainty, Statistics and Probability, Artificial Intelligence, Information Systems and General Decision Sciences, having authored 15 papers that have together received 712 indexed citations. Recurring topics across this work include Information Retrieval and Search Behavior (3 papers), Imbalanced Data Classification Techniques (3 papers), Probabilistic and Robust Engineering Design (3 papers), Advanced Statistical Methods and Models (2 papers), Expert finding and Q&A systems (2 papers), Machine Learning and Data Classification (2 papers), Advanced Statistical Process Monitoring (1 paper) and School Choice and Performance (1 paper). The work is most often cited by research in Artificial Intelligence (322 citations), Industrial and Manufacturing Engineering (83 citations), Statistics and Probability (58 citations), Health Information Management (22 citations) and Management of Technology and Innovation (37 citations). David Mease has collaborated with scholars based in United States. Frequent co-authors include Abraham J. Wyner, Andreas Buja, Vijayan N. Nair, Justin Bleich, Matthew Olson, Agus Sudjianto, Neema Moraveji, Jacob Bien, Miriam Louise Matteson and Daniel M. Russell. Their work appears in journals such as Journal of Machine Learning Research, Technometrics, The American Statistician, The Library Quarterly and Statistica Sinica.
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.