Theory and methods


  • Seismic vulnerability functions for Australian buildings by using GEM empirical vulnerability assessment guidelines. Maqsood, T., Edwards, M., Ioannou, I., Kosmidis, I., Rossetto, T., Corby, N. (2016). Natural Hazards, 80, 1625-1650.
  • Liquidity commonality does not imply liquidity resilience commonality: A functional characterisation for ultra-high frequency cross-sectional LOB data. Panayi, E., Peters, G. W., Kosmidis, I. (2015). Quantitative Finance, 15, 1737-1758 (also available at ArXiv e-prints, arXiv:1406.5486).
  • Upside and downside risk exposures of currency carry trades via tail dependence. Ames, M., Peters, G. W., Bagnarosa, G. and Kosmidis, I. (2015). In: Glau, M. Scherer, and R. Zagst (Eds.), Innovations in Quantitative Risk Management, Volume 99 of Springer Proceedings in Mathematics Statistics, pp. 163-181. (also available at ArXiv e-prints, arXiv:1406.4322).

Preprints packman

  • trackeR: Infrastructure for running and cycling data from GPS-enabled tracking devices in R. Frick, H. and Kosmidis, I. (2016). trackeR package vignette zip.
  • Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression. Kosmidis, I., Guolo, A. and Varin, C.. (2015). ArXiv e-prints, arXiv:1509.00650.
  • Linking the performance of endurance runners to training and physiological effects via multi-resolution elastic net. Kosmidis, I. and Passfield, L. (2015). ArXiv e-prints, arXiv:1506.01388.
  • Supervised sampling for clustering large data sets. Kosmidis, I., Karlis, D. (2010). CRiSM working paper 10-10.
  • On iterative adjustment of responses for the reduction of bias in binary regression models. Kosmidis, I. (2009). CRiSM working paper 09-36.

PhD thesis

Selected presentations

Projects for prospective PhD students

Below is a list of topics that I am highly interested in and I am/would be keen to research collaborating with a PhD student. Feel free to contact me if you are interested on any of those. Information on the application process and on available funding opportunities can be found here.

Optimal estimation and inference

  • Inference for models with many nuisance parameters (in progress)
  • The bias of functional estimators
  • Improved estimation for generalized linear mixed effects models (in progress)

Inferece from massive data sets

  • Estimation and inference in generalized linear models from massive data sets

Mixture models and applications

  • Model-based clustering with many variables

Applications in Sports

The above list is in no case exhaustive; if you have something else in mind and you are interested in working with me I would be more than happy to discuss.