Wai‐Yin Poon

1.8k citations
85 papers · 1.3k indexed · h-index 17

Wai‐Yin Poon

80 papers receiving 1.3k citations

Peers

Wai‐Yin Poon
Comparison fields: 5 of 134
  • Statistics and Probability 761
  • Management Science and Operations Research 271
  • Statistics, Probability and Uncertainty 97
  • Computational Mathematics 6
  • Applied Psychology 41
Replace Rolf Langeheine with:
Rolf Langeheine Germany
Gerhard Arminger Germany
Theodore Micceri United States
Zvi Gilula Israel
George B. Macready United States
Xinyuan Song Hong Kong
Edgar C. Merkle United States
Germán Molina United States
Cameron R. Peterson United States
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Citations per field
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Citations per year

Countries citing papers authored by Wai‐Yin Poon

Since Specialization
Citations

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

Fields of papers citing papers by Wai‐Yin Poon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wai‐Yin Poon. 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 Wai‐Yin Poon. The network helps show where Wai‐Yin Poon may publish in the future.

Co-authorship network

The 22 scholars most cited alongside Wai‐Yin Poon, 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 Wai‐Yin Poon Line = papers co-authored together Wai‐Yin Poon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20220
3 20144
4 20137
5 201117
6 20103
7 200921
8 200915
9 20082
10 20074
11 20067
12 200422
13 20022
14 200216
15 20017
16 20007
17 19993
18 1995194
19 19925
20
Errata for Maximum Likelihood Estimation of Multivariate Polyserial and Polychoric Correlation Coefficients.
19880

About Wai‐Yin Poon

Wai‐Yin Poon is a scholar working on Statistics and Probability, Computational Mathematics and Statistics, Probability and Uncertainty, having authored 85 papers that have together received 1.3k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (39 papers), Advanced Statistical Methods and Models (39 papers), Statistical Methods and Inference (17 papers), Statistical Methods in Clinical Trials (15 papers), Optimal Experimental Design Methods (12 papers), Sensory Analysis and Statistical Methods (9 papers), Advanced Statistical Process Monitoring (6 papers) and Statistical Distribution Estimation and Applications (6 papers). The work is most often cited by research in Statistics and Probability (761 citations), Management Science and Operations Research (271 citations) and Statistics, Probability and Uncertainty (97 citations). Wai‐Yin Poon has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Sik‐Yum Lee, Peter M. Bentler, Yat Sun Poon, Man‐Lai Tang, Henry S. Kingdon, Kwok Leung, Siu Hung Cheung, Niansheng Tang, Hoi Shan Kwan and Paul Lam. Their work appears in journals such as Statistics in Medicine, Journal of the Royal Statistical Society Series B (Statistical Methodology) and Psychometrika.

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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