Dr Sharon Lee
Dr

Sharon Lee

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Availability

Dr Sharon Lee is:
Available for supervision

Qualifications

  • Doctor of Philosophy, The University of Queensland

Search Professor Sharon Lee’s works on UQ eSpace

45 works between 2010 and 2025
All (45) Journal Article (27) Book Chapter (4) Conference Publication (14)

21 - 40 of 45 works

2017

Journal Article

Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution

Lin, Tsung-I, Wang, Wan-Lun, McLachlan, Geoffrey J. and Lee, Sharon X. (2017). Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution. Statistical Modelling, 18 (1), 50-72. doi: 10.1177/1471082X17718119

Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution

2017

Conference Publication

Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering

Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering. 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia, 1 - 4 August 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/Trustcom/BigDataSE/ICESS.2017.356

Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering

2017

Conference Publication

Mining high-dimensional CyTOF data: Concurrent gating, outlier removal, and dimension reduction

Lee, Sharon X. (2017). Mining high-dimensional CyTOF data: Concurrent gating, outlier removal, and dimension reduction. 28th Australasian Database Conference, ADC 2017, Brisbane, QLD, 25–28 September 2017. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-68155-9_14

Mining high-dimensional CyTOF data: Concurrent gating, outlier removal, and dimension reduction

2017

Book Chapter

Finite mixture models in biostatistics

Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Disease Modelling and Public Health, Part A. (pp. 75-102) edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne and C.R. Rao. Amsterdam, Netherlands: Elsevier. doi: 10.1016/bs.host.2017.08.005

Finite mixture models in biostatistics

2017

Conference Publication

Privacy distributed three-party learning of Gaussian mixture models

Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Privacy distributed three-party learning of Gaussian mixture models. International Conference on Applications and Technologies in Information Security (ATIS), Auckland, New Zealand, 6-7 July 2017. Singapore: Springer Singapore. doi: 10.1007/978-981-10-5421-1_7

Privacy distributed three-party learning of Gaussian mixture models

2016

Journal Article

Partial identification in the statistical matching problem

Ahfock, Daniel, Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Partial identification in the statistical matching problem. Computational Statistics and Data Analysis, 104, 79-90. doi: 10.1016/j.csda.2016.06.005

Partial identification in the statistical matching problem

2016

Journal Article

Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas

McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas. Statistics & Probability Letters, 116, 1-5. doi: 10.1016/j.spl.2016.04.004

Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas

2016

Journal Article

Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models

Lee, Sharon X and McLachlan, Geoffrey J (2016). Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models. Statistics and Computing, 26 (3), 573-589. doi: 10.1007/s11222-015-9545-x

Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models

2016

Conference Publication

On mixture modelling with multivariate skew distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2016). On mixture modelling with multivariate skew distributions. COMPSTAT: International Conference on Computational Statistics, Oviedo, Spain, 23-26 August 2016. The Hague, The Netherlands: The International Statistical Institute/International Association for Statistical Computing.

On mixture modelling with multivariate skew distributions

2016

Journal Article

Extending mixtures of factor models using the restricted multivariate skew-normal distribution

Lin, Tsung-I, McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Extending mixtures of factor models using the restricted multivariate skew-normal distribution. Journal of Multivariate Analysis, 143, 398-413. doi: 10.1016/j.jmva.2015.09.025

Extending mixtures of factor models using the restricted multivariate skew-normal distribution

2016

Conference Publication

Unsupervised component-wise EM learning for finite mixtures of skew t-distributions

Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Unsupervised component-wise EM learning for finite mixtures of skew t-distributions. 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, 12-15 December 2016. New York, NY, United States: Springer. doi: 10.1007/978-3-319-49586-6_49

Unsupervised component-wise EM learning for finite mixtures of skew t-distributions

2016

Journal Article

Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure. Cytometry Part A, 89 (1), 30-43. doi: 10.1002/cyto.a.22789

Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure

2016

Conference Publication

A simple parallel EM algorithm for statistical learning via mixture models

Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2016). A simple parallel EM algorithm for statistical learning via mixture models. International Conference on Digital Image Computing, Gold Coast, QLD, Australia, 30 November - 2 December,2016. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/DICTA.2016.7796997

A simple parallel EM algorithm for statistical learning via mixture models

2016

Book Chapter

Application of mixture models to large datasets

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Application of mixture models to large datasets. Big data analytics: methods and applications. (pp. 57-74) edited by Saumyadipta Pyne, B. L. S. Prakasa Rao and S. B. Rao. New Delhi, India: Springer India. doi: 10.1007/978-81-322-3628-3_4

Application of mixture models to large datasets

2015

Journal Article

Nature and man: the goal of bio-security in the course of rapid and inevitable human development

Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2015). Nature and man: the goal of bio-security in the course of rapid and inevitable human development. Journal of the Indian Society of Agricultural Statistics, 69 (2), 117-125.

Nature and man: the goal of bio-security in the course of rapid and inevitable human development

2014

Journal Article

A robust factor analysis model using the restricted skew-t distribution

Lin, Tsung-I, Wu, Pal H., McLachlan, Geoffrey J. and Lee, Sharon X. (2014). A robust factor analysis model using the restricted skew-t distribution. Test, 24 (3), 510-531. doi: 10.1007/s11749-014-0422-2

A robust factor analysis model using the restricted skew-t distribution

2014

Journal Article

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

Pyne, Saumyadipta, Lee, Sharon X., Wang, Kui, Irish, Jonathan, Tamayo, Pablo, Nazaire, Marc-Danie, Duong, Tarn, Ng, Shu-Kay, Hafler, David, Levy, Ronald, Nolan, Garry P., Mesirov, Jill and McLachlan, Geoffrey J. (2014). Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. PLoS One, 9 (7) e100334, e100334.1-e100334.11. doi: 10.1371/journal.pone.0100334

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

2014

Journal Article

Finite mixtures of multivariate skew t-distributions: Some recent and new results

Lee, Sharon and McLachlan, Geoffrey J. (2014). Finite mixtures of multivariate skew t-distributions: Some recent and new results. Statistics and Computing, 24 (2), 181-202. doi: 10.1007/s11222-012-9362-4

Finite mixtures of multivariate skew t-distributions: Some recent and new results

2013

Journal Article

EMMIXuskew: An R package for Fitting Mixtures of Multivariate Skew t distributions via the EM algorithm

Lee S.X. and McLachlan G.J. (2013). EMMIXuskew: An R package for Fitting Mixtures of Multivariate Skew t distributions via the EM algorithm. Journal of Statistical Software, 55 (12), 1-22. doi: 10.18637/jss.v055.i12

EMMIXuskew: An R package for Fitting Mixtures of Multivariate Skew t distributions via the EM algorithm

2013

Journal Article

Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions"

Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions". Statistical Methods and Applications, 22 (4), 473-479. doi: 10.1007/s10260-013-0249-0

Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions"

Current funding

  • 2023 - 2026
    A Novel Approach to Semi-Supervised Statistical Machine Learning
    ARC Discovery Projects
    Open grant

Past funding

  • 2018 - 2022
    Classification methods for providing personalised and class decisions
    ARC Discovery Projects
    Open grant
  • 2016 - 2019
    Flexible data modelling via skew mixture models:challenges and applications
    ARC Discovery Early Career Researcher Award
    Open grant

Availability

Dr Sharon Lee is:
Available for supervision

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Supervision history

Current supervision

  • Doctor Philosophy

    Role of Finite Mixture Models in Semi-Supervised Learning

    Associate Advisor

    Other advisors: Professor Geoffrey McLachlan

Enquiries

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