Hani Doss

2.6k citations
47 papers · 1.9k · h-index 20

Impact in

Papers in

    • Statistical Methods and Inference 24
    • Statistical Methods and Bayesian Inference 18
    • Statistical Distribution Estimation and Applications 10
    • Markov Chains and Monte Carlo Methods 5
    • Bayesian Methods and Mixture Models 21
    • Gaussian Processes and Bayesian Inference 3

Hani Doss

46 papers receiving 1.8k citations

Peers

Hani Doss
Comparison fields: 5 of 164
  • Geriatrics and Gerontology 233
  • Statistics and Probability 593
  • Artificial Intelligence 477
  • Pharmacology 180
  • Paleontology 71
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Citations per field
00.5×10×20×30×35.5×
Eugene F. Schuster · 1×
Citations per year

Countries citing papers authored by Hani Doss

Since Specialization
Citations

This map shows the geographic impact of Hani Doss'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 Hani Doss with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hani Doss more than expected).

Fields of papers citing papers by Hani Doss

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hani Doss. 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 Hani Doss. The network helps show where Hani Doss may publish in the future.

Co-authors

The 25 scholars most cited alongside Hani Doss, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hani Doss Line = papers co-authored together Hani Doss links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2005352
2 2014279
3 2000183
4 2005103
5 200589
6 201479
7 199477
8 200561
9 200357
10 199656
11 199351
12 201848
13 199248
14 200043
15 199542
16 198537
17 198529
18 198223
19 198923
20 198921

About Hani Doss

Hani Doss is a scholar working on Statistics and Probability, Artificial Intelligence, Statistics, Probability and Uncertainty, Demography and Molecular Biology, having authored 47 papers that have together received 1.9k indexed citations. Recurring topics across this work include Statistical Methods and Inference (24 papers), Bayesian Methods and Mixture Models (21 papers), Statistical Methods and Bayesian Inference (18 papers), Statistical Distribution Estimation and Applications (10 papers), Markov Chains and Monte Carlo Methods (5 papers), Insurance, Mortality, Demography, Risk Management (3 papers), Gaussian Processes and Bayesian Inference (3 papers) and Probabilistic and Robust Engineering Design (2 papers). The work is most often cited by research in Geriatrics and Gerontology (233 citations), Statistics and Probability (593 citations), Artificial Intelligence (477 citations), Pharmacology (180 citations) and Paleontology (71 citations). Hani Doss has collaborated with scholars based in United States, Taiwan and Netherlands. Frequent co-authors include Deborah Doss, Randall E. Harris, Dennis K. Pearl, Shuying Li, Deborah Burr, Todd M. Manini, Richard D. Gill, Xiaotong Shen, Yufeng Liu and Jayaram Sethuraman. Their work appears in journals such as The Annals of Statistics, Journal of the American Statistical Association, Journal of Computational and Graphical Statistics, Journal of Applied Probability and Probability Theory and Related Fields.

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.

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