• Statistics & Econometrics  (primary)

  • Finite mixture & latent class modeling

  • Computer intensive & Bayesian methods

  • Classification & clustering methods

  • Model selection & dimension

  • Bootstrap & bagging methods

  • Generalized linear mixed models

  • Spatial modeling

   

• Marketing Research & Modeling  (secondary)

  • Statistics & econometric modeling in marketing

  • Consumer behaviour & demographic modeling

  • Conjoint analysis & choice modeling

  • Market heterogeneity & segmentation techniques

  • Market share modeling & market simulation

   

• Population & Health Economics  (secondary)

  • Modeling demographic and health microdata

  • Social marketing applications

2. Refereed publications

1. Dias, J.G. (2004), Controlling the level of separation of components in Monte Carlo studies of latent class models, in Banks, D., L. House, F.R. McMorris, P. Arabie, and W. Gaul (eds.), Classification, Clustering, and Data Mining Applications, Berlin: Springer-Verlag, 77-84.

2. Dias, J.G. and M. Wedel (2004), On EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods, Statistics and Computing, 14(4), 323-332.

3. Dias, J.G. (2005), Bootstrapping latent class models, Weihs, C. and W. Gaul (eds.), Classification - The Ubiquitous Challenge, Berlin: Springer-Verlag, 121-128.

4. Dias, J.G. and F.J. Willekens (2005), Model-based clustering of sequential data with an application to contraceptive use dynamics, Mathematical Population Studies, 12(3), 135-157.

5. Dias, J.G. (2006), Latent class analysis and model selection, in Spiliopoulou, M., R. Kruse, C. Borgelt, A. Nürnberger, W. Gaul (eds.), From Data and Information Analysis to Knowledge Engineering, Berlin: Springer-Verlag, 95-102.

6. Padmadas, S.S., J.G. Dias and F.J. Willekens (2006), Disentangling women’s responses on complex dietary intake patterns from an Indian cross-sectional survey: A latent class analysis, Public Health Nutrition, 9(2), 204–211.

7. Dias, J.G. (2006), Model selection for the binary latent class model. A Monte Carlo simulation, Springer-Verlag, in Batagelj, V., H.-H. Bock, A. Ferligoj, and A. Žiberna (eds.), Data Science and Classification, Berlin: Springer-Verlag, 91-99.

8. Dias, J.G. and J.K. Vermunt (2006), Bootstrap methods for measuring classification uncertainty in latent class models, in Rizzi, A. and M. Vichi (eds.), COMPSTAT2006. Proceedings in Computational Statistics, Heidelberg: Physica/Springer-Verlag, 31-41.

9. Dias, J.G., and J.K. Vermunt, Latent class modeling of website users' search patterns: Implications for online market segmentation, Journal of Retailing and Consumer Services, forthcoming.

3. Other publications & Working papers

1. Dias, J.G. (2001), Components of knowledge on AIDS in Brazil. Identifying information needs using a segmented approach, Working Paper 01-3, December, Population Research Centre, University of Groningen.

2. Dias, J.G. (2002), Finite mixture models with applications to demography, Master Theses Series, Groningen: Population Research Centre.

3. Dias, J.G. and M. Kathun (2002), Modelling the choice of contraceptive methods incorporating unobserved heterogeneity, Working paper, SOM Research Centre, University of Groningen.

4. Padmadas, S.S., J.G. Dias, and F. Willekens (2003), Understanding dietary intake behaviour of women in India: A latent class approach, Applications and Policy Working Papers, Southampton Statistical Sciences Research Institute.

5. Dias, J.G. (2004), Finite Mixture Models. Review, Applications, and Computer-intensive Methods (PhD Thesis), The Netherlands: Ridderprint. ISBN: 90-5335-037-3.

6. Daniel A. Griffith, Spatial Autocorrelation and Spatial Filtering. Gaining Understanding Throught Theory and Scientific Visualization, Berlin: Springer-Verlag, 2003, 247 pp., in European Spatial Research and Policy, 2005, 12(1), 162-165 (Book review).