Dr Tereza Neocleous
- Senior Lecturer (Statistics)
Applied statistician, interested in developing flexible models that enhance our understanding of data and facilitate inference.
- '98 - BA in Mathematical Tripos, Cambridge
- '00 - MSc in Statistics, University of Illinois Urbana-Champaign
- '05 - PhD in Statistics, University of Illinois Urbana-Champaign
My research interests include quantile regression, survival data analysis, semiparametric models and multivariate data analysis. My main areas of application are biostatistics, epidemiology, forensic statistics, chemometrics and linguistics.
Lee, D. and Neocleous, T. (2010) Bayesian quantile regression for count data with application to environmental epidemiology. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59(5), pp. 905-920. (doi: 10.1111/j.1467-9876.2010.00725.x)
Chanialidis, C. , Evers, L. , Neocleous, T. and Nobile, A. (2018) Efficient Bayesian inference for COM-Poisson regression models. Statistics and Computing, 28(3), pp. 595-608. (doi: 10.1007/s11222-017-9750-x)
Biosa, G., Giurghita, D., Alladio, E., Vincenti, M. and Neocleous, T. (2020) Evaluation of forensic data using logistic regression-based classification methods and an R Shiny implementation. Frontiers in Chemistry, 8, 738. (doi: 10.3389/fchem.2020.00738) (PMID:33195014) (PMCID:PMC7609892)
Dimitra Eleftheriou (Ph.D. 2017-), jointly supervised with L. Evers. Modelling biomarkers for clinical and sports applications.
Taweesak Changgam (Ph.D 2020-), jointly supervised with C. Anderson. Models for child growth.
Jorge Sanchez (Ph.D. 2020-), jointly supervised with N. Dean. Models for forensic spectroscopy data.
Catherine Holland (Ph.D. 2020-), jointly supervised with G. Napier. Bayesian models for compositional data.