Joan G. Staniswalis

1.5k citations
37 papers · 1.1k indexed · h-index 17

Joan G. Staniswalis

36 papers receiving 1.1k citations

Peers

Joan G. Staniswalis
Comparison fields: 5 of 131
  • Statistics and Probability 716
  • Health, Toxicology and Mutagenesis 143
  • Statistics, Probability and Uncertainty 59
  • Finance 67
  • Management Science and Operations Research 81
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Citations per year

Countries citing papers authored by Joan G. Staniswalis

Since Specialization
Citations

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

Fields of papers citing papers by Joan G. Staniswalis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joan G. Staniswalis. 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 Joan G. Staniswalis. The network helps show where Joan G. Staniswalis may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Joan G. Staniswalis, 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 Joan G. Staniswalis Line = papers co-authored together Joan G. Staniswalis links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201612
2 201512
3 20152
4 20123
5 201167
6 200935
7 20013
8
Fitting the additive model by recursion on dimension
20000
9 1998131
10 199721
11 19942
12 19935
13 19938
14 19922
15 199120
16 199123
17 199033
18 1989125
19 198936
20 198879

About Joan G. Staniswalis

Joan G. Staniswalis is a scholar working on Statistics and Probability, Management Science and Operations Research, Health, Toxicology and Mutagenesis, Environmental Engineering and Applied Mathematics, having authored 37 papers that have together received 1.1k indexed citations. Recurring topics across this work include Statistical Methods and Inference (16 papers), Statistical Methods and Bayesian Inference (10 papers), Advanced Statistical Methods and Models (7 papers), Statistical Distribution Estimation and Applications (7 papers), Air Quality and Health Impacts (5 papers), Optimal Experimental Design Methods (5 papers), Climate Change and Health Impacts (4 papers) and Statistical Methods in Clinical Trials (4 papers). The work is most often cited by research in Statistics and Probability (716 citations), Health, Toxicology and Mutagenesis (143 citations), Statistics, Probability and Uncertainty (59 citations), Finance (67 citations) and Management Science and Operations Research (81 citations). Joan G. Staniswalis has collaborated with scholars based in United States. Frequent co-authors include Thomas A. Severini, J. Jack Lee, Sara E. Grineski, Eleanor D. Campbell, Chris Gennings, Kimber L. White, Walter H. Carter, Thomas E. Gill, Yolanda Muñoz Maldonado and Peter F. Thall. Their work appears in journals such as Journal of the American Statistical Association, Journal of Statistical Computation and Simulation, Statistics and Computing, The American Statistician and Journal of the Air & Waste Management Association.

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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