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Professor You-Gan Wang
Chief Investigator
Queensland University of Technology
Professor Wang’s research interests include developing statistical methodology for correlated data analysis, robust inferences and model selection and applying advanced techniques that help to solve important problems in medical sciences, environmental research and natural resource management.
Currently, he is a member of ARC College of Experts, and on the Editorial Board of Biometrics and Environmental Modeling and Assessment. Professor Wang also has extensive experience in initiating research projects and experience in designing experiments, data analysis and working with environmental researchers, marine biologists and others. Professor Wang has been very successful in grant applications including FRDC (Fisheries Research and Development Corporation) and ARC DP and Linkage grants. He has experience in training students and researchers with effective courses and programs and engaging with government and industry partners. His work is usually motivated by practical need.
In statistics, Professor Wang is interested in developing methodologies in the main stream of statistics, particularly in the area of clustered/longitudinal data analysis to model the covariance and model selection criteria. His work has been published in Biometrika, Biometrics , Journal of the American Statistical Association, Statistical Methods in Medical Research, Annals of Statistics, Statistics in Medicine and Biostatistics.
In knowledge-based applied statistics, Professor Wang has extensive experience in initiating research projects and designing experiments, data analysis and working with environmental researchers, marine biologists. He contributed as main or sole author to many other disciplines including publications by Journal of Hydrology , Water Resources Research, Geophysical Research Letters, Envoironmental Science & Technology, Phytopathology, Fisheries etc.
Research Interests:
Applied statistics
Biostatistics
Data Science
Developing Statistical Methodology
Environmantal statistics
Longitudinal Data Analysis
Model Selection
Robust Inferences
Statistical modelling
Qualifications:
D.Phil (Statistics), Oxford
1991 Statistics D.Phil, University of Oxford (Supervised by Prof JC Gittins) 1988 Statistics M.Sc, Peking University 1986 Mathematics B.Sc, Zhejiang University
Mathematics B.Sc, Zhejiang University
Projects
Publications
Books
Wang, Y-G., Fu L., & Paul S.
(2022). Analysis of Longitudinal Data with Examples.
248. doi: 10.1201/9781315153636
Invited talks, refereed proceedings and other conference outputs
Hu, S., Li Y.., Ingham A.., Hurley G.. B., Gonzalez E.. G., & Wang Y-G.
(2019). Comparison of Machine Learning Algorithms in Classification of Grazing Behaviour in Sheep.
The 20th INFORMS Applied Probability Society Conference.
Wang, Y-G., & Wu J.
(2019). Chaotic time series regression modeling using phase space reconstruction and deep neural network.
The 20th INFORMS Applied Probability Society Conference.
Journal Articles
Duan, Q., McGrory C. A., Brown G., Mengersen K., & Wang Y-G.
(2021). Spatio-temporal quantile regression analysis revealing more nuanced patterns of climate change: a study of long-term daily temperature in Australia.
arXiv. arXiv:2103.05791v1.
Liu, C., Zhou S., Wang Y-G., & Hu Z.
(2020). Natural mortality estimation using tree-based ensemble learning models.
ICES Journal of Marine Science. 77(4), 1414-1426. doi: 10.1093/icesjms/fsaa058
Wei, C., Hu Z-H., & Wang Y-G.
(2020). Exact algorithms for energy-efficient virtual machine placement in data centers.
Future Generation Computer Systems. 106, 77 - 91. doi: 10.1016/j.future.2019.12.043
Jin, G., Xu J., Mo Y., Tang H., Wei T., Wang Y-G., et al.
(2020). Response of sediments and phosphorus to catchment characteristics and human activities under different rainfall patterns with Bayesian Networks.
Journal of Hydrology. 584, 124695. doi: 10.1016/j.jhydrol.2020.124695
Ding, Z., Tian Y-C., Tang M., Li Y., Wang Y-G., & Zhou C.
(2020). Profile-guided three-phase virtual resource management for energy efficiency of data centers.
IEEE Transactions on Industrial Electronics. 67(3), 8663624, 2460-2468. doi: 10.1109/TIE.2019.2902786
Gao, S., Wu J., Stiller J., Zheng Z., Zhou M., Wang Y-G., et al.
(2020). Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.
Theoretical and Applied Genetics. 133(9), 2535 - 2544. doi: 10.1007/s00122-020-03615-y
Wu, J., Wang Y-G., Burrage K., Tian Y-C., Lawson B., & Ding Z.
(2020). An improved firefly algorithm for global continuous optimization problems.
Expert Systems with Applications. 149, 113340. doi: 10.1016/j.eswa.2020.113340
Xu, J., Jin G., Tang H., Mo Y., Wang Y-G., & Li L.
(2019). Response of water quality to land use and sewage outfalls in different seasons.
Science of The Total Environment. 696, 134014. doi: 10.1016/j.scitotenv.2019.134014
Jiang, Y., Wang Y-G., Fu L., & Wang X.
(2019). Robust Estimation Using Modified Huber’s Functions With New Tails.
Technometrics. 61(1), 111-122. doi: 10.1080/00401706.2018.1470037
Wu, J., Wang N., Hu Z-H., Hong Z., & Wang Y-G.
(2019). Incorporating Social Objectives in Evaluating Sustainable Fisheries Harvest Strategy.
Environmental Modeling & Assessment. 24(4), 381-386. doi: 10.1007/s10666-019-9651-9
Wu, J., Cui Z., Chen Y., Kong D., & Wang Y-G.
(2019). A new hybrid model to predict the electrical load in five states of Australia.
Energy. 166, 598 - 609. doi: 10.1016/j.energy.2018.10.076
Ullah, I., Paul S., Hong Z., & Wang Y-G.
(2019). Significance tests for analyzing gene expression data with small sample sizes.
Bioinformatics. 35(20), 3996 - 4003. doi: 10.1093/bioinformatics/btz189
Wang, N., Wang Y-G., Hu S., Hu Z-H., Xu J., Tang H., et al.
(2018). Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations.
Environmental Modeling & Assessment. 23(6), 779-786. doi: 10.1007/s10666-018-9605-7
Fu, L., Hao Y., & Wang Y-G.
(2018). Working correlation structure selection in generalized estimating equations.
Computational Statistics. 33(2), 983 - 996. doi: 10.1007/s00180-018-0800-4
Li, B., Zhang L., Wang Y-G., George A. W., Reverter A., & Li Y.
(2018). Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning MethodsTable_1.pdf.
Frontiers in Genetics. 9, 237. doi: 10.3389/fgene.2018.00237
