Dr Wei Zhang
- Honorary Research Fellow (School of Mathematics & Statistics)
https://orcid.org/0000-0002-7554-5115Biography
I joined the School as a Lecturer in Statistics in May 2021. Before that I obtained my PhD in statistics from the University of Auckland (2018), and held two postdoctoral fellowships at Western University (2017-2019) and the University of California, Berkeley (2019-2021).
Research interests
My research mainly focuses on developing statistical models for analysing ecological data and computationally efficient methods for fitting complicated models. I am particularly interested in capture-recapture and spatial capture-recapture problems. I am also interested in computational statistics. Currently I am contributing to some R packages including nimble and nimbleSCR.
Research groups
Publications
2024
Bonner, S. J., Zhang, W. and Mu, J. (2024) On the identifiability of the trinomial model for mark‐recapture‐recovery studies. Environmetrics, 35(1), e2827. (doi: 10.1002/env.2827)
2023
Zhang, W. , Bonner, S. J. and McCrea, R. (2023) Latent multinomial models for extended batch-mark data. Biometrics, 79(3), pp. 2732-2742. (doi: 10.1111/biom.13789) (PMID:36321329) (PMCID:PMC10953401)
Zhang, W. , Chipperfield, J. D., Illian, J. B. , Dupont, P., Milleret, C., de Valpine, P. and Bischof, R. (2023) A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. Ecology, 104(1), e3887. (doi: 10.1002/ecy.3887) (PMID:36217822)
2021
Zhang, W. , Price, S. J. and Bonner, S. J. (2021) Maximum likelihood inference for the band-read error model for capture-recapture data with misidentification. Environmental and Ecological Statistics, 28(2), pp. 405-422. (doi: 10.1007/s10651-021-00492-6)
2020
Zhang, W. and Bonner, S. J. (2020) On continuous‐time capture‐recapture in closed populations. Biometrics, 76(3), pp. 1028-1033. (doi: 10.1111/biom.13185) (PMID:31823352)
2019
Zhang, W. , Bravington, M.V. and Fewster, R.M. (2019) Fast likelihood‐based inference for latent count models using the saddlepoint approximation. Biometrics, 75(3), pp. 723-733. (doi: 10.1111/biom.13030) (PMID:30690707)
Zhang, W. , Liu, J., Goodman, J., Weir, B. S. and Fewster, R. M. (2019) Stationary distribution of the linkage disequilibrium coefficient r2. Theoretical Population Biology, 128, pp. 19-26. (doi: 10.1016/j.tpb.2019.05.002) (PMID:31145877) (PMCID:PMC7262955)
2018
Yu, C., Zhang, W. , Xu, X., Ji, Y. and Yu, S. (2018) Data mining based multi-level aggregate service planning for cloud manufacturing. Journal of Intelligent Manufacturing, 29(6), pp. 1351-1361. (doi: 10.1007/s10845-015-1184-8)
Articles
Bonner, S. J., Zhang, W. and Mu, J. (2024) On the identifiability of the trinomial model for mark‐recapture‐recovery studies. Environmetrics, 35(1), e2827. (doi: 10.1002/env.2827)
Zhang, W. , Bonner, S. J. and McCrea, R. (2023) Latent multinomial models for extended batch-mark data. Biometrics, 79(3), pp. 2732-2742. (doi: 10.1111/biom.13789) (PMID:36321329) (PMCID:PMC10953401)
Zhang, W. , Chipperfield, J. D., Illian, J. B. , Dupont, P., Milleret, C., de Valpine, P. and Bischof, R. (2023) A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. Ecology, 104(1), e3887. (doi: 10.1002/ecy.3887) (PMID:36217822)
Zhang, W. , Price, S. J. and Bonner, S. J. (2021) Maximum likelihood inference for the band-read error model for capture-recapture data with misidentification. Environmental and Ecological Statistics, 28(2), pp. 405-422. (doi: 10.1007/s10651-021-00492-6)
Zhang, W. and Bonner, S. J. (2020) On continuous‐time capture‐recapture in closed populations. Biometrics, 76(3), pp. 1028-1033. (doi: 10.1111/biom.13185) (PMID:31823352)
Zhang, W. , Bravington, M.V. and Fewster, R.M. (2019) Fast likelihood‐based inference for latent count models using the saddlepoint approximation. Biometrics, 75(3), pp. 723-733. (doi: 10.1111/biom.13030) (PMID:30690707)
Zhang, W. , Liu, J., Goodman, J., Weir, B. S. and Fewster, R. M. (2019) Stationary distribution of the linkage disequilibrium coefficient r2. Theoretical Population Biology, 128, pp. 19-26. (doi: 10.1016/j.tpb.2019.05.002) (PMID:31145877) (PMCID:PMC7262955)
Yu, C., Zhang, W. , Xu, X., Ji, Y. and Yu, S. (2018) Data mining based multi-level aggregate service planning for cloud manufacturing. Journal of Intelligent Manufacturing, 29(6), pp. 1351-1361. (doi: 10.1007/s10845-015-1184-8)
Teaching
- Advanced Predictive Models (STATS5098)
- Bayesian Statistics (STATS5100)
- Stochastic Processes (STATS4024/5026)