Research Publications for Lynette A (Lyn) Hunt

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Publications ByHUNT, Lynette A (Lyn)

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  • Hunt, L. A., & Basford, K. E. (2017). Comparing classical criteria for selecting intra-class correlated features for three-mode three-way data. In The International Biometric Society Australasian Region Conference (IBS-AR 2017). Conference held Gold Coast, Australia.

  • Hunt, L. A., & Jorgensen, M. (2017). The multimix class of mixture models for clustering mixed categorical and continuous data. In 1st International Conference on Econometrics and Statistics (EcoSta 2017). Conference held in Hong Kong.

  • Hunt, L. A. (2016). Mixture model selection for three-mode three-way data. In 2016 International Biometric Conference. Victoria, Canada.

  • Hunt, L. A., & Basford, K. E. (2016). Comparing classical criteria for selecting intra-class correlated features in Multimix. Computational Statistics & Data Analysis, 103, 350-366. doi:10.1016/j.csda.2016.05.018

  • Hunt, L. A., & Basford, K. (2016). Comparing classical criteria for selecting intra-class correlated features for three-mode three-way data. In 2016 AUT Mathematical Sciences Symposium (pp. 15). Auckland, NZ.

  • Hunt, L. (2015). Missing data imputation and its effect on the accuracy of classification. In Conference of the International Federation of Classification Societies, Book of Abstracts (pp. 105). Bologna, Italy: ifcs.

  • Liang, E. S., Tipper, G., Hunt, L. A., & Gan, P. Y. C. (2015). Cranioplasty outcomes and associated complications: a single centre observational study. British Journal of Neurosurgery, 30(1), 122-127. doi:10.3109/02688697.2015.1080216

  • Hunt, L. A. (2014). Model selection for the Multimix class of Mixture Models. In MTO Colloquium, Tilburg University, Tilburg, Netherlands.

  • Hunt, L. A. (2014). An evaluation of missing data imputation and its effect on classifier performance. Poster session presented at the meeting of 27th International Biometric Conference. Florence.

  • Hunt, L. A., Stubbs, R. S., & Hayes, M. (2014). Predictive models for resolution of Type 2 diabetes in patients with gastric bypass. In Centre de Recherche Public de la Sante, Luxembourg.


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