V. N. LaRiccia
- Statistics and Probability top 1%
- Statistical Methods and Inference 28
- Statistical Distribution Estimation and Applications 17
- Advanced Statistical Methods and Models 15
- Statistical Methods and Bayesian Inference 14
- Environmental Engineering top 5%
- Finance top 5%
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- Bayesian Methods and Mixture Models 11
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- Medical Imaging Techniques and Applications 4
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- Sparse and Compressive Sensing Techniques 3
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- Statistical and numerical algorithms 3
- Co-authors
- P. P. B. EggermontR. L. EubankSteen MagnussenRebecca B. RosensteinJeffrey D. HartDavid M. MasonThomas E. WehrlyJohn H. Schuenemeyer
- Cited by
- Statistics and ProbabilityNature and Landscape ConservationStatistics, Probability and Uncertainty
- Journals
- Journal of the American Statistical Association (12 papers)IEEE Transactions on Information Theory (1 paper)Journal of neurosurgery (1 paper)
- Partner nations
- United States
In The Last Decade
V. N. LaRiccia
45 papers receiving 748 citations
Peers
Comparison fields: 5 of 92
- Statistics and Probability 473
- Nature and Landscape Conservation 144
- Statistics, Probability and Uncertainty 83
- Environmental Engineering 165
- Finance 93
Countries citing papers authored by V. N. LaRiccia
This map shows the geographic impact of V. N. LaRiccia'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 V. N. LaRiccia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites V. N. LaRiccia more than expected).
Fields of papers citing papers by V. N. LaRiccia
This network shows the impact of papers produced by V. N. LaRiccia. 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 V. N. LaRiccia. The network helps show where V. N. LaRiccia may publish in the future.
Co-authorship network
The 13 scholars most cited alongside V. N. LaRiccia, 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 | Maximum Penalized Likelihood Estimation: Volume II Regression | 2011 | 11 |
| 2 | 2003 | 1 | |
| 3 | 2003 | 2 | |
| 4 | 2000 | 20 | |
| 5 | 1999 | 162 | |
| 6 | ON EM-LIKE ALGORITHMS FOR MINIMUM DISTANCE ESTIMATION | 1998 | 5 |
| 7 | 1997 | 13 | |
| 8 | 1996 | 8 | |
| 9 | 1995 | 6 | |
| 10 | 1993 | 26 | |
| 11 | 1992 | 15 | |
| 12 | 1990 | 1 | |
| 13 | 1989 | 22 | |
| 14 | 1987 | 30 | |
| 15 | 1986 | 9 | |
| 16 | 1986 | 10 | |
| 17 | 1985 | 11 | |
| 18 | 1985 | 2 | |
| 19 | 1984 | 2 | |
| 20 | 1983 | 4 |
About V. N. LaRiccia
V. N. LaRiccia is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence, having authored 46 papers that have together received 841 indexed citations. Recurring topics across this work include Statistical Methods and Inference (28 papers), Statistical Distribution Estimation and Applications (17 papers), Advanced Statistical Methods and Models (15 papers), Statistical Methods and Bayesian Inference (14 papers), Bayesian Methods and Mixture Models (11 papers), Medical Imaging Techniques and Applications (4 papers), Sparse and Compressive Sensing Techniques (3 papers) and Statistical and numerical algorithms (3 papers). The work is most often cited by research in Statistics and Probability (473 citations), Nature and Landscape Conservation (144 citations) and Statistics, Probability and Uncertainty (83 citations). V. N. LaRiccia has collaborated with scholars based in United States. Frequent co-authors include P. P. B. Eggermont, R. L. Eubank, Steen Magnussen, Rebecca B. Rosenstein, Jeffrey D. Hart, David M. Mason, Thomas E. Wehrly, John H. Schuenemeyer, William P. Brown and Roland R. Roth. Their work appears in journals such as Journal of the American Statistical Association, IEEE Transactions on Information Theory and Journal of neurosurgery.
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.