Yuling Yao
Impact in
- Statistics and Probability top 10%
- Statistical Methods and Bayesian Inference
- Statistical Methods and Inference
Papers in
-
- Statistical Methods and Bayesian Inference 2
- Statistical Methods and Inference 2
-
- COVID-19 Pandemic Impacts 2
- Co-authors
- Andrew Gelman (6 shared papers)Aki Vehtari (3 shared papers)Daniel Simpson (2 shared papers)Paul‐Christian Bürkner (1 shared paper)Jonah Gabry (1 shared paper)Måns Magnusson (1 shared paper)Topi Paananen (1 shared paper)Alexander van Geen (3 shared papers)
- Journals
- Physical review. D (1 paper)JAMA Network Open (1 paper)Journal of Physics G Nuclear and Particle Physics (1 paper)Computational Brain & Behavior (1 paper)GeoHealth (1 paper)
- Partner nations
- United StatesCanadaFinland
In The Last Decade
Yuling Yao
11 papers receiving 195 citations
Peers
Comparison fields: 5 of 110
- Statistics and Probability 30
- General Decision Sciences 6
- Nature and Landscape Conservation 22
- Computational Mathematics 1
- Experimental and Cognitive Psychology 21
Countries citing papers authored by Yuling Yao
This map shows the geographic impact of Yuling Yao'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 Yuling Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuling Yao more than expected).
Fields of papers citing papers by Yuling Yao
This network shows the impact of papers produced by Yuling Yao. 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 Yuling Yao. The network helps show where Yuling Yao may publish in the future.
Co-authors
The 25 scholars most cited alongside Yuling Yao, 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 | Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models [R package loo version 2.4.1] | 2020 | 53 |
| 2 | 2018 | 48 | |
| 3 | 2017 | 24 | |
| 4 | 2024 | 21 | |
| 5 | 2020 | 17 | |
| 6 | 2021 | 14 | |
| 7 | 2020 | 9 | |
| 8 | 2021 | 6 | |
| 9 | Preventive maintenance timing of asphalt pavement | 2006 | 4 |
| 10 | Yes, but did it work?: Evaluating variational inference | 2018 | 2 |
| 11 | 2021 | 1 |
About Yuling Yao
Yuling Yao is a scholar working on Statistics and Probability, Economics and Econometrics, Statistics, Probability and Uncertainty, Modeling and Simulation and Health, having authored 11 papers that have together received 199 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (2 papers), COVID-19 epidemiological studies (2 papers), Statistical Methods and Inference (2 papers), COVID-19 Pandemic Impacts (2 papers), Infrastructure Maintenance and Monitoring (1 paper), Transport Systems and Technology (1 paper), Heavy Metal Exposure and Toxicity (1 paper) and COVID-19 impact on air quality (1 paper). The work is most often cited by research in Statistics and Probability (30 citations), General Decision Sciences (6 citations), Nature and Landscape Conservation (22 citations), Computational Mathematics (1 citation) and Experimental and Cognitive Psychology (21 citations). Yuling Yao has collaborated with scholars based in United States, Canada and Finland. Frequent co-authors include Andrew Gelman, Aki Vehtari, Daniel Simpson, Paul‐Christian Bürkner, Jonah Gabry, Måns Magnusson, Topi Paananen, Alexander van Geen, Yu‐Sung Su and Donald Lien. Their work appears in journals such as Physical review. D, JAMA Network Open, Journal of Physics G Nuclear and Particle Physics, Computational Brain & Behavior and GeoHealth.
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.