J. Sunil Rao

3.2k citations
77 papers · 1.9k indexed · 1 hit paper · h-index 20

J. Sunil Rao

70 papers receiving 1.9k citations

Hit Papers

Spike and slab variable selection: Frequentist and Bayesi...20052026201220192005200400600

Peers

J. Sunil Rao
Comparison fields: 5 of 171
  • Molecular Biology 497
  • Statistics and Probability 474
  • Artificial Intelligence 335
  • Surgery 222
  • Genetics 165
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Citations per year

Countries citing papers authored by J. Sunil Rao

Since Specialization
Citations

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

Fields of papers citing papers by J. Sunil Rao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. Sunil Rao. 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 J. Sunil Rao. The network helps show where J. Sunil Rao may publish in the future.

Co-authorship network of co-authors of J. Sunil Rao

This figure shows the co-authorship network connecting the top 25 collaborators of J. Sunil Rao. A scholar is included among the top collaborators of J. Sunil Rao 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 J. Sunil Rao. J. Sunil Rao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 5
3 0
4 4
5 25
6 2
7
Epidemiological and Molecular Features of Prostate Cancer in Asian Men Living in Asian Countries: Implications in Screen and Management
1
8
Observed best prediction via nested-error regression with potentially misspecified mean and variance
5
9 22
10 25
11
Fence method for nonparametric small area estimation
15
12 3
13 1
14 1
15 34
16 11
17
Bootstrap choice of cost complexity for better subset selection
8
18 14
19
Visualizing bagged decision trees
18
20 24

About J. Sunil Rao

J. Sunil Rao is a scholar working on Statistics and Probability, Neuropsychology and Physiological Psychology and Applied Microbiology and Biotechnology, having authored 77 papers that have together received 1.9k indexed citations. Recurring topics across this work include Statistical Methods and Inference (18 papers), Gene expression and cancer classification (15 papers) and Statistical Methods and Bayesian Inference (13 papers). The work is most often cited by research in Statistics and Probability (474 citations), Computational Mathematics (8 citations) and Artificial Intelligence (335 citations). J. Sunil Rao has collaborated with scholars based in United States, China and Sweden. Frequent co-authors include Hemant Ishwaran, Jiming Jiang, Thuan Nguyen, Mary Levin, Peggy Sullivan, Anita P. Merriam, Chelliah Richmonds, Patrick Leahy, Francisco H. Andrade and Henry J. Kaminski. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association and Journal of the American College of Cardiology.

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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