Dr Tereza Neocleous

  • Senior Lecturer (Statistics)

telephone: 01413306117
email: Tereza.Neocleous@glasgow.ac.uk

School Of Maths & Stats, Room 317 Maths & Stats Building, Phone: 330 6117, University Place, Glasgow, G12 8QQ

Import to contacts

ORCID iDhttps://orcid.org/0000-0001-7792-4541

Research interests

I am an applied statistician interested in developing flexible models that enhance our understanding of data to facilitate inference. My research focuses on modelling multilevel, multivariate data with applications in forensic science, health, the environment and social science.

Research groups

Publications

Selected publications

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)

Napier, G., Neocleous, T. and Nobile, A. (2015) A composite Bayesian hierarchical model of compositional data with zeros. Journal of Chemometrics, 9(2), pp. 96-108. (doi: 10.1002/cem.2681)

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)

All publications

List by: Type | Date

Jump to: 2022 | 2020 | 2018 | 2017 | 2016 | 2015 | 2014 | 2011 | 2010 | 2009 | 2008 | 2006
Number of items: 18.

2022

Szili, B., Niu, M. and Neocleous, T. (2022) A Structural Learning Method for Graphical Models. In: 4th International Conference on Statistics: Theory and Applications (ICSTA'22), Prague, Czech Republic, 28-30 Jul 2022, p. 113. ISBN 9781990800085 (doi: 10.11159/icsta22.113)

2020

Nic Daeid, N., Biedermann, A., Champod, C., Hutton, J., Jackson, G., Neocleous, T. , Spiegelhalter, D., Willis, S., Kitchin, D. and Wilson, A. (2020) The Use of Statistics in Legal Proceedings: A primer for the courts. Series: Primers for Courts. The Royal Society: United Kingdom. ISBN 9781782524861

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)

2018

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)

2017

Alexander, C. , Stuart-Smith, J. , Neocleous, T. and Evers, L. (2017) Using Chain Graph Models for Structural Inference With an Application to Linguistic Data. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 270-274.

2016

Martyna, A., Zadora, G., Neocleous, T. , Michalska, A. and Dean, N. (2016) Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra. Analytica Chimica Acta, 931, pp. 34-46. (doi: 10.1016/j.aca.2016.05.016) (PMID:27282749)

2015

Napier, G. , Nobile, A. and Neocleous, T. (2015) An online application for the classification and evidence evaluation of forensic glass fragments. Chemometrics and Intelligent Laboratory Systems, 146, pp. 418-425. (doi: 10.1016/j.chemolab.2015.06.013)

Napier, G., Neocleous, T. and Nobile, A. (2015) A composite Bayesian hierarchical model of compositional data with zeros. Journal of Chemometrics, 9(2), pp. 96-108. (doi: 10.1002/cem.2681)

2014

Chanialidis, C., Evers, L. , Neocleous, T. and Nobile, A. (2014) Retrospective sampling in MCMC with an application to COM-Poisson regression. Stat, 3(1), pp. 273-290. (doi: 10.1002/sta4.61)

2011

Neocleous, T. , Aitken, C. and Zadora, G. (2011) Transformations for compositional data with zeros with an application to forensic evidence evaluation. Chemometrics and Intelligent Laboratory Systems, 109(1), pp. 77-85. (doi: 10.1016/j.chemolab.2011.08.003)

2010

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)

Zadora, G. and Neocleous, T. (2010) Evidential value of physicochemical data-comparison of methods of glass database creation. Journal of Chemometrics, 24(7-8), pp. 367-378. (doi: 10.1002/cem.1276)

Dowlman, E. A., Martin, N. C., Foy, M. J., Lochner, T. and Neocleous, T. (2010) The prevalence of mixed DNA profiles on fingernail swabs. Science and Justice, 50(2), pp. 64-71. (doi: 10.1016/j.scijus.2009.03.005)

Zadora, G., Neocleous, T. and Aitken, C. (2010) A Two-Level Model for Evidence Evaluation in the Presence of Zeros. Journal of Forensic Sciences, 55(2), pp. 371-384. (doi: 10.1111/j.1556-4029.2009.01316.x)

2009

Neocleous, T. and Portnoy, S. (2009) Partially linear censored quantile regression. Lifetime Data Analysis, 15(3), pp. 357-378. (doi: 10.1007/s10985-009-9117-5)

Zadora, G. and Neocleous, T. (2009) Likelihood ratio model for classification of forensic evidence. Analytica Chimica Acta, 642(1-2), pp. 266-278. (doi: 10.1016/j.aca.2008.12.013)

2008

Neocleous, T. and Portnoy, S. (2008) On monotonicity of regression quantile functions. Statistics and Probability Letters, 78(10), pp. 1226-1229. (doi: 10.1016/j.spl.2007.11.024)

2006

Neocleous, T. , Branden, K. V. and Portnoy, S. (2006) Correction to censored regression quantiles by s. portnoy, 98 (2003), 1001–1012. Journal of the American Statistical Association, 101(474), pp. 860-861. (doi: 10.1198/016214506000000087)

This list was generated on Fri Mar 7 01:17:23 2025 GMT.
Jump to: Articles | Books | Conference Proceedings
Number of items: 18.

Articles

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)

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)

Martyna, A., Zadora, G., Neocleous, T. , Michalska, A. and Dean, N. (2016) Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra. Analytica Chimica Acta, 931, pp. 34-46. (doi: 10.1016/j.aca.2016.05.016) (PMID:27282749)

Napier, G. , Nobile, A. and Neocleous, T. (2015) An online application for the classification and evidence evaluation of forensic glass fragments. Chemometrics and Intelligent Laboratory Systems, 146, pp. 418-425. (doi: 10.1016/j.chemolab.2015.06.013)

Napier, G., Neocleous, T. and Nobile, A. (2015) A composite Bayesian hierarchical model of compositional data with zeros. Journal of Chemometrics, 9(2), pp. 96-108. (doi: 10.1002/cem.2681)

Chanialidis, C., Evers, L. , Neocleous, T. and Nobile, A. (2014) Retrospective sampling in MCMC with an application to COM-Poisson regression. Stat, 3(1), pp. 273-290. (doi: 10.1002/sta4.61)

Neocleous, T. , Aitken, C. and Zadora, G. (2011) Transformations for compositional data with zeros with an application to forensic evidence evaluation. Chemometrics and Intelligent Laboratory Systems, 109(1), pp. 77-85. (doi: 10.1016/j.chemolab.2011.08.003)

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)

Zadora, G. and Neocleous, T. (2010) Evidential value of physicochemical data-comparison of methods of glass database creation. Journal of Chemometrics, 24(7-8), pp. 367-378. (doi: 10.1002/cem.1276)

Dowlman, E. A., Martin, N. C., Foy, M. J., Lochner, T. and Neocleous, T. (2010) The prevalence of mixed DNA profiles on fingernail swabs. Science and Justice, 50(2), pp. 64-71. (doi: 10.1016/j.scijus.2009.03.005)

Zadora, G., Neocleous, T. and Aitken, C. (2010) A Two-Level Model for Evidence Evaluation in the Presence of Zeros. Journal of Forensic Sciences, 55(2), pp. 371-384. (doi: 10.1111/j.1556-4029.2009.01316.x)

Neocleous, T. and Portnoy, S. (2009) Partially linear censored quantile regression. Lifetime Data Analysis, 15(3), pp. 357-378. (doi: 10.1007/s10985-009-9117-5)

Zadora, G. and Neocleous, T. (2009) Likelihood ratio model for classification of forensic evidence. Analytica Chimica Acta, 642(1-2), pp. 266-278. (doi: 10.1016/j.aca.2008.12.013)

Neocleous, T. and Portnoy, S. (2008) On monotonicity of regression quantile functions. Statistics and Probability Letters, 78(10), pp. 1226-1229. (doi: 10.1016/j.spl.2007.11.024)

Neocleous, T. , Branden, K. V. and Portnoy, S. (2006) Correction to censored regression quantiles by s. portnoy, 98 (2003), 1001–1012. Journal of the American Statistical Association, 101(474), pp. 860-861. (doi: 10.1198/016214506000000087)

Books

Nic Daeid, N., Biedermann, A., Champod, C., Hutton, J., Jackson, G., Neocleous, T. , Spiegelhalter, D., Willis, S., Kitchin, D. and Wilson, A. (2020) The Use of Statistics in Legal Proceedings: A primer for the courts. Series: Primers for Courts. The Royal Society: United Kingdom. ISBN 9781782524861

Conference Proceedings

Szili, B., Niu, M. and Neocleous, T. (2022) A Structural Learning Method for Graphical Models. In: 4th International Conference on Statistics: Theory and Applications (ICSTA'22), Prague, Czech Republic, 28-30 Jul 2022, p. 113. ISBN 9781990800085 (doi: 10.11159/icsta22.113)

Alexander, C. , Stuart-Smith, J. , Neocleous, T. and Evers, L. (2017) Using Chain Graph Models for Structural Inference With an Application to Linguistic Data. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 270-274.

This list was generated on Fri Mar 7 01:17:23 2025 GMT.

Supervision

I welcome enquiries from students interested in PhD or MSc by Research projects in the following areas:

Forensic statistics

Multivariate data analysis for hierarchical/longitudinal data

Quantile regression applications in health and social science

Current PhD supervision:

Completed student research projects:

Taweesak Channgam (Ph.D. 2020-2024, jointly supervised with C. Anderson). Flexible quantile regression and Bayesian quantile modelling for longitudinal child growth data.

Jorge Sanchez (Ph.D. 2020-2024, jointly supervised with N. Dean). Variable selection for supervised and semi-supervised mixtures of contaminated Gaussian distributions.

Benjamin Szili (Ph.D. 2018-22, jointy supervised with M. Niu). Structural learning for continuous data using graphical models.

Dimitra Eleftheriou (Ph.D. 2017-22). Bayesian hierarchical modelling for biomarkers with applications to doping detection and prostate cancer prediction.

Craig Alexander (Ph.D. 2014-18, jointly supervised with L. Evers and J. Stuart-Smith). Multilevel models for the analysis of linguistic data.

Charalampos Chanialidis (Ph.D., 2011-15, jointly supervised with L. Evers). Bayesian mixture models for count data.

Gary Napier (Ph.D., 2010-14, jointly supervised with A. Nobile). A Bayesian hierarchical model of compositional data with zeros: classification and evidence evaluation of forensic glass.

Elizabeth Irwin (M.Sc. by research, 2012-13). Statistical methods of constructing growth charts.

Laura Allison (M.Sc. by research, 2010-11). Evaluation of transfer evidence.

Gary Napier (M.Sc. by research, 2009-10, jointly supervised with S. Senn). Modelling obesity in Scotland.

Teaching

I teach a variety of statistics courses and supervise projects at both the undergraduate and postgraduate level. My teaching philosophy is centered around the belief that learning is most effective when it's hands-on. I design my courses with this in mind, aiming to encourage active participation and learning by doing. In addition to in-person teaching, in recent years I have worked on designing and delivering online courses and assessments for the Online MSc in Data Analytics.

Additional information

Personal webpage: 

https://www.maths.gla.ac.uk/~tereza/


A portrait of Tereza Neocleous