Thomas J. DiCiccio
- Statistics and Probability top 0.1%
- Statistical Methods and Inference 37
- Statistical Methods and Bayesian Inference 31
- Advanced Statistical Methods and Models 22
- Statistical Distribution Estimation and Applications 14
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- Probabilistic and Robust Engineering Design 5
- General Decision Sciences top 5%
- Artificial Intelligence top 1%
- Bayesian Methods and Mixture Models 11
- Bayesian Modeling and Causal Inference 6
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- Financial Risk and Volatility Modeling 4
Thomas J. DiCiccio
67 papers receiving 4.7k citations
Hit Papers
Peers
Comparison fields: 5 of 212
- Statistics and Probability 2.3k
- Statistics, Probability and Uncertainty 481
- General Decision Sciences 73
- Management Science and Operations Research 413
- Artificial Intelligence 1.0k
Countries citing papers authored by Thomas J. DiCiccio
This map shows the geographic impact of Thomas J. DiCiccio'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 Thomas J. DiCiccio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas J. DiCiccio more than expected).
Fields of papers citing papers by Thomas J. DiCiccio
This network shows the impact of papers produced by Thomas J. DiCiccio. 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 Thomas J. DiCiccio. The network helps show where Thomas J. DiCiccio may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Thomas J. DiCiccio, 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 | Learning during the COVID-19 pandemic: It is not who you teach, but how you teachbreakdown → | 2021 | 105 |
| 2 | 2020 | 20 | |
| 3 | 2017 | 2 | |
| 4 | 2012 | 2 | |
| 5 | 2012 | 42 | |
| 6 | Computing Bayes Factors by Combining Simulation and Asymptotic Approximationsbreakdown → | 1997 | 1516 |
| 7 | Bootstrap Condence Intervals | 1996 | 21 |
| 8 | 1996 | 46 | |
| 9 | Bootstrap confidence intervalsbreakdown → | 1996 | 1616 |
| 10 | 1995 | 4 | |
| 11 | Alternative Aspects of Conditional Inference. A Discussion of "The Roles of Conditioning in Inference" by N. Reid | 1995 | 1 |
| 12 | Constructing Approximately Standard Normal Pivots from Signed Roots of Adjusted Likelihood Ratio Statistics | 1994 | 13 |
| 13 | 1994 | 65 | |
| 14 | 1993 | 28 | |
| 15 | 1992 | 6 | |
| 16 | 1991 | 12 | |
| 17 | 1991 | 55 | |
| 18 | 1989 | 101 | |
| 19 | 1987 | 59 | |
| 20 | 1987 | 30 |
About Thomas J. DiCiccio
Thomas J. DiCiccio is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence, having authored 70 papers that have together received 5.1k indexed citations. Recurring topics across this work include Statistical Methods and Inference (37 papers), Statistical Methods and Bayesian Inference (31 papers), Advanced Statistical Methods and Models (22 papers), Statistical Distribution Estimation and Applications (14 papers), Bayesian Methods and Mixture Models (11 papers), Bayesian Modeling and Causal Inference (6 papers), Probabilistic and Robust Engineering Design (5 papers) and Financial Risk and Volatility Modeling (4 papers). The work is most often cited by research in Statistics and Probability (2.3k citations), Statistics, Probability and Uncertainty (481 citations) and General Decision Sciences (73 citations). Thomas J. DiCiccio has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Bradley Efron, Robert E. Kass, Adrian E. Raftery, Larry Wasserman, Joseph P. Romano, Michael A. Martin, Peter Hall, Steven Stern, Anna Clara Monti and G. A. Young.
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.