Shu‐Kay Ng
Impact in
- Artificial Intelligence top 0.5%
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
- Bayesian Methods and Mixture Models
- Advanced Clustering Algorithms Research
- Health Information Management top 0.5%
Papers in
-
- Statistical Methods and Bayesian Inference 12
- Statistical Distribution Estimation and Applications 11
- Statistical Methods and Inference 10
- Co-authors
- Geoffrey J. McLachlanZhi‐Hua ZhouQiang YangMichael SteinbachHiroshi MotodaDan SteinbergJoydeep GhoshBing Liu
- Journals
- Statistics in Medicine (6 papers)Psycho-Oncology (5 papers)International Journal of Infectious Diseases (4 papers)Oncogene (4 papers)European Journal of Cancer (4 papers)
- Partner nations
- AustraliaUnited StatesHong Kong
In The Last Decade
Shu‐Kay Ng
165 papers receiving 7.3k citations
Hit Papers
Peers
Comparison fields: 5 of 221
- Artificial Intelligence 2.2k
- Health Information Management 246
- Statistics and Probability 349
- Information Systems 910
- Obstetrics and Gynecology 310
Countries citing papers authored by Shu‐Kay Ng
This map shows the geographic impact of Shu‐Kay Ng'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 Shu‐Kay Ng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shu‐Kay Ng more than expected).
Fields of papers citing papers by Shu‐Kay Ng
This network shows the impact of papers produced by Shu‐Kay Ng. 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 Shu‐Kay Ng. The network helps show where Shu‐Kay Ng may publish in the future.
Co-authors
The 25 scholars most cited alongside Shu‐Kay Ng, 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 | 2025 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 0 | |
| 4 | 2023 | 2 | |
| 5 | 2023 | 9 | |
| 6 | 2023 | 1 | |
| 7 | 2022 | 11 | |
| 8 | 2020 | 4 | |
| 9 | 2020 | 6 | |
| 10 | 2020 | 36 | |
| 11 | 2020 | 5 | |
| 12 | 2018 | 33 | |
| 13 | 2018 | 39 | |
| 14 | 2018 | 0 | |
| 15 | 2016 | 4 | |
| 16 | 2016 | 34 | |
| 17 | 2013 | 22 | |
| 18 | Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees | 2003 | 1 |
| 19 | 2003 | 10 | |
| 20 | CHANGE OF IMAGE | 2002 | 2 |
About Shu‐Kay Ng
Shu‐Kay Ng is a scholar working on Medical Terminology, Statistics and Probability, Obstetrics and Gynecology, Geriatrics and Gerontology and Reproductive Medicine, having authored 179 papers that have together received 7.7k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (26 papers), Gene expression and cancer classification (13 papers), Statistical Methods and Bayesian Inference (12 papers), Statistical Distribution Estimation and Applications (11 papers), Chronic Disease Management Strategies (10 papers), Statistical Methods and Inference (10 papers), Health Systems, Economic Evaluations, Quality of Life (10 papers) and Bioinformatics and Genomic Networks (10 papers). The work is most often cited by research in Artificial Intelligence (2.2k citations), Health Information Management (246 citations), Statistics and Probability (349 citations), Information Systems (910 citations) and Obstetrics and Gynecology (310 citations). Shu‐Kay Ng has collaborated with scholars based in Australia, United States and Hong Kong. Frequent co-authors include Geoffrey J. McLachlan, Zhi‐Hua Zhou, Qiang Yang, Michael Steinbach, Hiroshi Motoda, Dan Steinberg, Joydeep Ghosh, Bing Liu, Philip S. Yu and David J. Hand. Their work appears in journals such as Statistics in Medicine, Psycho-Oncology, International Journal of Infectious Diseases, Oncogene and European Journal of Cancer.
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.