Dr Wei Zhang

  • Lecturer (Statistics)

Biography

I joined the School of Mathematics and Statistics 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 units

Publications

List by: Type | Date

Jump to: 2022 | 2021 | 2020 | 2019 | 2018
Number of items: 7.

2022

Zhang, W. , Bonner, S. J. and McCrea, R. (2022) Latent multinomial models for extended batch-mark data. Biometrics, (doi: 10.1111/biom.13789) (PMID:36321329) (Early Online Publication)

Zhang, W. , Chipperfield, J. D., Illian, J. B. , Dupont, P., Milleret, C., de Valpine, P. and Bischof, R. (2022) A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. Ecology, (doi: 10.1002/ecy.3887) (PMID:36217822) (In Press)

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)

This list was generated on Mon Dec 5 07:42:02 2022 GMT.
Jump to: Articles
Number of items: 7.

Articles

Zhang, W. , Bonner, S. J. and McCrea, R. (2022) Latent multinomial models for extended batch-mark data. Biometrics, (doi: 10.1111/biom.13789) (PMID:36321329) (Early Online Publication)

Zhang, W. , Chipperfield, J. D., Illian, J. B. , Dupont, P., Milleret, C., de Valpine, P. and Bischof, R. (2022) A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. Ecology, (doi: 10.1002/ecy.3887) (PMID:36217822) (In Press)

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)

This list was generated on Mon Dec 5 07:42:02 2022 GMT.

Teaching

  • Advanced Predictive Models (STATS5098)
  • Bayesian Statistics (STATS5100)
  • Stochastic Processes (STATS4024/5026)