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Dr Nicole White
Associate Investigator, Research Fellow
Queensland University of Technology
Dr. White is a postdoctoral researcher in Statistics, with key research interests in Bayesian methodology and it applications in health. In 2011, she received her PhD under the supervision of Professor Kerrie Mengersen, entitled “Bayesian mixtures for complex medical data: A case study in Parkinson’s disease”.
Since receiving her PhD, Dr. White has been involved in a number of collaborative research projects at QUT within the School of Mathematical Sciences and the Institute for Health and Biomedical Innovation.
Research Interests:
Applied statistics
Bayesian statistics
Bioinformatics
Health services research
Spatial Epidemiology
Qualifications:
Bachelor of Mathematics
Bachelor of Applied Science (Honours)
PhD (Statistics)
Projects
Publications
Book Chapters
Mengersen, KL., Duncan E., Arbel J., Alston-Knox C., & White N.
(2019). Applications in Industry.
(Fruhwirth-Schnatter, S., Celeux G., & Robert C. P., Ed.).Handbook of Mixture Analysis.
Invited talks, refereed proceedings and other conference outputs
White, N., Mengersen KL., & Lea R.
(2017). Probabilistic deconvolution of microarray data using a hierarchical Bayesian model.
Royal Statistical Society Conference 2017.
Journal Articles
Kennedy, DW., White N., Benton M. C., Fox A., Scott R. J., Griffiths L. R., et al.
(2018). Critical evaluation of linear regression models for cell-subtype specific methylation signal from mixed blood cell DNA.
(Tost, J., Ed.).PLOS ONE. 13(12), e0208915. doi: 10.1371/journal.pone.0208915
Maltby, V. E., Lea R. A., Saunders K., White N., Benton M. C., Scott R. J., et al.
(2017). Differential methylation at MHC in CD4+ T cells is associated with multiple sclerosis independently of HLA-DRB1.
Clinical Epigenetics. 9(1), doi: 10.1186/s13148-017-0371-1
White, N., Benton M., Kennedy DW., Fox A., Griffiths L., Lea R., et al.
(2017). Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm.
PLoS ONE. 12, Article number: e0182455. doi: 10.1371/journal.pone.0182455
Thomas, A., Toms L-M. L., Harden F. A., Hobson P., White N., Mengersen KL., et al.
(2017). Concentrations of organochlorine pesticides in pooled human serum by age and gender.
Environmental Research. 154, 10-18. doi: 10.1016/j.envres.2016.12.009
Baker, J., White N., Mengersen KL., Rolfe M., & Morgan G. G.
(2017). Joint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia.
(Ali, M., Ed.).PLOS ONE. 12(8), e0183653. doi: 10.1371/journal.pone.0183653
Kang, S. Y., Cramb S., White N., Ball S. J., & Mengersen KL.
(2016). Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data.
Geospatial Health. 11(2), doi: 10.4081/gh.2016.428
Duncan, EW., White N., & Mengersen KL.
(2016). Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation.
BMJ Open. 6(5), doi: 10.1136/bmjopen-2015-010253
White, N., & Mengersen KL.
(2016). Predicting health programme participation: A gravity-based, hierarchical modelling approach.
Journal of the Royal Statistical Society: Series C (Applied Statistics). 65(1), 145-166. doi: 10.1111/rssc.12111
van Havre, Z., White N., Rousseau J., Mengersen KL., & Chen C. W. S.
(2015). Overfitting Bayesian Mixture Models with an Unknown Number of Components.
PLOS ONE. 10(7), doi: 10.1371/journal.pone.0131739
Cramb, S., Baade P. D., White N., Ryan L. M., & Mengersen KL.
(2015). Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis.
Cancer Epidemiology. 39(3), 430-439. doi: 10.1016/j.canep.2015.03.001
Baker, J., White N., & Mengersen KL.
(2015). Spatial modelling of type II diabetes outcomes: a systematic review of approaches used.
Royal Society Open Science. 2(6), 140460. doi: 10.1098/rsos.140460
Technical reports and unrefereed outputs
Kennedy, DW., White N., Mengersen KL., & Lea R.
(Submitted). Cell-type specific analysis of heterogeneous methylation signal using a Bayesian model-based approach.
Cramb, S., Duncan EW., White N., Baade P. D., & Mengersen KL.
(2016). Spatial Modelling Methods.
Kennedy, DW., White N., Lea R., Benton M., Mengersen KL., & Griffiths L.
(2016). Evaluating linear regression routines for deconvolution of methylation signal from whole blood.
