Jane-Ling Wang
- Statistics and Probability top 0.1%
- Statistical Methods and Inference 45
- Statistical Methods and Bayesian Inference 27
- Statistical Distribution Estimation and Applications 9
- Advanced Statistical Methods and Models 8
- Advanced Causal Inference Techniques 7
- Aging top 2%
- Computational Mathematics top 5%
- Artificial Intelligence top 1%
- Bayesian Methods and Mixture Models 12
-
- Insect behavior and control techniques 10
-
- Functional Brain Connectivity Studies 7
Jane-Ling Wang
94 papers receiving 4.2k citations
Hit Papers
Peers
Comparison fields: 5 of 183
- Statistics and Probability 2.3k
- Aging 109
- Computational Mathematics 27
- Statistics, Probability and Uncertainty 263
- Artificial Intelligence 986
Countries citing papers authored by Jane-Ling Wang
This map shows the geographic impact of Jane-Ling Wang'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 Jane-Ling Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jane-Ling Wang more than expected).
Fields of papers citing papers by Jane-Ling Wang
This network shows the impact of papers produced by Jane-Ling Wang. 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 Jane-Ling Wang. The network helps show where Jane-Ling Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jane-Ling Wang, 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 | 2024 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2023 | 2 | |
| 4 | 2022 | 1 | |
| 5 | Deep Extended Hazard Models for Survival Analysis | 2021 | 14 |
| 6 | 2020 | 71 | |
| 7 | 2019 | 3 | |
| 8 | 2018 | 5 | |
| 9 | 2016 | 22 | |
| 10 | 2014 | 36 | |
| 11 | 2011 | 7 | |
| 12 | Covariate adjusted functional principal components analysis for longitudinal\n data | 2010 | 51 |
| 13 | 2010 | 65 | |
| 14 | 2009 | 20 | |
| 15 | 2009 | 62 | |
| 16 | 2003 | 87 | |
| 17 | 2002 | 15 | |
| 18 | 2001 | 9 | |
| 19 | M-estimators for Censored Data: Strong Consistency | 1995 | 8 |
| 20 | 1989 | 9 |
About Jane-Ling Wang
Jane-Ling Wang is a scholar working on Statistics and Probability, Aging and Insect Science, having authored 97 papers that have together received 4.3k indexed citations. Recurring topics across this work include Statistical Methods and Inference (45 papers), Statistical Methods and Bayesian Inference (27 papers), Bayesian Methods and Mixture Models (12 papers), Insect behavior and control techniques (10 papers), Statistical Distribution Estimation and Applications (9 papers), Advanced Statistical Methods and Models (8 papers), Advanced Causal Inference Techniques (7 papers) and Functional Brain Connectivity Studies (7 papers). The work is most often cited by research in Statistics and Probability (2.3k citations), Aging (109 citations) and Computational Mathematics (27 citations). Jane-Ling Wang has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include Hans‐Georg Müller, Fang Yao, Jeng‐Min Chiou, Xiaoke Zhang, Ci‐Ren Jiang, James R. Carey, Pablo Liedo, Linda M. Styer, James R. Carey and Thomas W. Scott. Their work appears in journals such as The Annals of Statistics, Biometrika, Journal of the American Statistical Association, Experimental Gerontology and Journal of Multivariate Analysis.
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.