# Dr Nema Dean

**Lecturer**(Statistics)

**telephone**: 01413306820

**email**: Nema.Dean@glasgow.ac.uk

R230 Level 2

Mathematics Building

15 University Gardens

Glasgow G12 8QW

### Research Interests

My research interests are in developing new clustering and classification methods. Past work has involved research on finite mixture model based methods and variations that incorporate variable selection and semi-supervised updating. Currently I am working on creating hybrid clustering methods using both parametric and classical algorithmic approaches. I have also developed new mixture model clustering methods for discrete and space-restricted data. Social network analysis and dynamic treatment regimes are also current areas of interest. Application areas I have looked at include: housing markets, cDNA microarrays, electronic educational testing, food authenticity studies and many others. I am on the committee for an International Federation of Classification Societies initiative to promote good benchmarking practices in clustering research.

#### Research Groups

## Selected publications | View all publications

Dean, N., and Nugent, R.
(2013)
Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas.
*Advances in Data Analysis and Classification*, 7(3),
pp. 339-357.
(doi:10.1007/s11634-013-0149-z)

Moodie, E. E.M., Dean, N., and Sun, Y. R.
(2014)
Q-learning: flexible learning about useful utilities.
*Statistics in Biosciences*, 6(2),
pp. 223-243.
(doi:10.1007/s12561-013-9103-z)

Dean, N., and Raftery, A. E.
(2010)
Latent class analysis variable selection.
*Annals of the Institute of Statistical Mathematics*, 62(1),
pp. 11-35.
(doi:10.1007/s10463-009-0258-9)

Murphy, T.B., Dean, N., and Raftery, A.E.
(2010)
Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications.
*Annals of Applied Statistics*, 4(1),
pp. 396-421.
(doi:10.1214/09-AOAS279)

## All publications | View selected publications

### Current PhD students

### Postgraduate opportunities

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