# 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

## Research interests

I am an applied statistician interested in developing flexible models that enhance our understanding of data and facilitate inference. 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.

### Research units

- Statistics & Data Analytics
- Nonparametric & semi-parametric statistics
- Biostatistics, epidemiology, & health applications
- Social & urban studies
- Statistics in chemistry/physics

## 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

## 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:

Taweesak Changgam (Ph.D 2020-), jointly supervised with C. Anderson. Models for child growth.

Jorge Sanchez (Ph.D. 2020-), jointly supervised with N. Dean. Classification using finite mixtures of contaminated normal distributions.

Catherine Holland (Ph.D. 2020-), jointly supervised with G. Napier and O. Stoner. Bayesian models for compositional data.

**Channgam**, Taweesak

Models for longitudinal child growth data**Holland**, Catherine

Bayesian approaches to compositional data with structural zeros**Sanchez Gomez**, Jorge Alfredo

Model-based inference for geocoded data with application to crime patterns

Completed student research projects:

Dimitra Eleftheriou (Ph.D. 2017-2022). 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 student projects at the undergraduate and postgraduate level. I am interested in ways of teaching that encourage student engagement and interaction, and in designing assignments that enable students to learn 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.