My core research interests are in the theory and methodology of statistical learning and inference, and, in particular, in penalized and pseudo-likelihood theory and methods, statistical computing and algorithms for regression problems and methods for clustering.

I also engage in interdisciplinary applied work of the kind that involves a real synthesis of approaches and has the potential to generate new advances in statistical learning and inference with broader impact. The applied work I am/have been involved in is in: sports science (modelling of high-frequency in-game events in team sports, and uncovering the links between human behaviour, health, fitness and overall well-being); finance (modelling the dynamics of financial indicators with structural dependencies); earthquake engineering (assessment of the vulnerability of the built environment from post-hazard survey data); neuroimaging (regression methods for brain lesions from MRI data and the summarization and visualization of effects); and genetics (infering changes in genomic network structures).

Last but not least, I am particularly interested in the design and development of scientific (and not only) software, to deliver the advances from my theoretical/methodological efforts and some tools that I find useful to the Data Science community and more broadly.

Research groups and themes

Some groups and themes that I founded or participate/have participated are:

Preprints and unpublished reports

  • Kosmidis I, Zietkiewicz P (2023). Jeffreys-prior penalty for high-dimensional logistic regression: A conjecture about aggregate bias.
    ArXiV Supplementary material Theory Methods

  • Sterzinger P, Kosmidis I (2023). Diaconis-Ylvisaker prior penalized likelihood for \(p /n \to \kappa \in (0, 1)\) logistic regression.
    ArXiV Supplementary material Theory Methods

  • Zietkiewicz P, Kosmidis I (2023). Bounded-memory adjusted scores estimation in generalized linear models with large data sets.
    ArXiV Supplementary material Theory Methods

  • Kindalova P, Veldsman M, Nichols T E, Kosmidis I (2021). Penalized generalized estimating equations for relative risk regression with applications to brain lesion data.
    bioRxiv Methods Applications

  • Köll S, Kosmidis I, Kleiber C, Zeileis A (2021). Bias reduction as a remedy to the consequences of infinite estimates in poisson and tobit regression.
    ArXiV Theory Methods

  • Firth D, Kosmidis I, Turner H L (2019). Davidson-Luce model for multi-item choice with ties.
    ArXiV Theory

  • Karimalis E, Kosmidis I, Peters G W (2017). Multi yield curve stress-testing framework incorporating temporal and cross tenor structural dependencies
    SSRN Bank of England Staff Working Paper Series Methods Applications

  • Kosmidis I, Passfield L (2015). Linking the performance of endurance runners to training and physiological effects via multi-resolution elastic net.
    ArXiV Applications Methods

  • Kosmidis I, Karlis D (2010). Supervised sampling for clustering large data sets.
    CRiSM Working Paper Series Applications Methods

  • Kosmidis I (2010). On iterative adjustment of responses for the reduction of bias in binary regression models.
    CRiSM Working Paper Series Methods Theory


  • Kindalova P, Kosmidis I, Nichols T E (2021). Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework.
    NeuroImage, 236, 118090
    DOI bioRxiv Methods Applications

  • Kosmidis I, Firth D (2021). Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models.
    Biometrika, 108, 71–82
    DOI ArXiV Supplementary material Theory Methods

  • Whitaker G A, Silva R, Edwards D, Kosmidis I (2021). A Bayesian inference approach for determining player abilities in football.
    Journal of the Royal Statistical Society: Series C, 70, 174-201
    DOI ArXiV Methods Applications
  • Veldsman M, Kindalova P, Husain M, Kosmidis I, Nichols T E (2020). Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities.
    NeuroImage: Clinical, 28, 102405
    DOI bioRxiv Methods Applications
  • Kosmidis I, Kenne Pagui E C, Sartori N (2020). Mean and median bias reduction in generalized linear models.
    Statistics and Computing, 30, 43-59
    DOI ArXiV Methods Theory
  • Di Caterina C, Kosmidis I (2019). Location-adjusted Wald statistic for scalar parameters.
    Computational Statistics and Data Analysis, 138, 126-142
    DOI ArXiv Methods Theory

  • Kyriakou S, Kosmidis I, Sartori N (2019). Median bias reduction in random-effects meta-analysis and meta-regression.
    Statistical Methods in Medical Research, 28, 1622-1636
    DOI ArXiV Supplementary material Methods Applications

  • Tsokos A, Narayanan S, Kosmidis I, Baio G, Cucuringu M, Whitaker G, Király F J (2019). Modeling outcomes of soccer matches.
    Machine Learning, 108, 77-95
    DOI ArXiV Applications

  • Ioannou I, Bessason B, Kosmidis I, Bjarnason J Ö, Rossetto T (2018). Empirical seismic vulnerability assessment of Icelandic buildings affected by the 2000 sequence of earthquakes.
    Bulletin of Earthquake Engineering, 16, 5875–5903
    DOI Applications

  • Frick H, Kosmidis I (2017). trackeR: Infrastructure for running and cycling data from GPS-enabled tracking devices in R.
    Journal of Statistical Software, 82
    DOI Software Methods Applications

  • Kosmidis I, Guolo A, Varin C (2017). Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression.
    Biometrika, 104, 489-496
    DOI ArXiV Theory Methods Applications

  • Kosmidis I, Karlis D (2016). Model-based clustering using copulas with applications.
    Statistics and Computing, 26, 1079–1099
    DOI ArXiV Methods Applications

  • Maqsood T, Edwards M, Ioannou I, Kosmidis I, Rossetto T, Corby N (2016). Seismic vulnerability functions for Australian buildings by using GEM empirical vulnerability assessment guidelines.
    Natural Hazards, 80, 1625-1650
    DOI Applications

  • Panayi E, Peters G W, Kosmidis I (2015). Liquidity commonality does not imply liquidity resilience commonality: A functional characterisation for ultra-high frequency cross-sectional LOB data.
    Quantitative Finance, 15, 1737-1758
    DOI ArXiV Applications

  • Ames M, Peters G W, Bagnarosa G, Kosmidis I (2015). Upside and downside risk exposures of currency carry trades via tail dependence.
    In: Glau, K, Scherer, M and Zagst, R (Eds.), Innovations in Quantitative Risk Management, Volume 99 of Springer Proceedings in Mathematics Statistics, 163-181
    DOI ArXiV Applications Methods

  • Kosmidis I (2014). Bias in parametric estimation: reduction and useful side-effects.
    WIRE Computational Statistics, 6, 185-196
    DOI ArXiV Methods

  • Kosmidis I (2014). Improved estimation in cumulative link models.
    Journal of the Royal Statistical Society: Series B, 76, 169-196
    DOI ArXiV Supplementary Theory Methods

  • Grün B, Kosmidis I, Zeileis A (2012). Extended Beta regression in R: Shaken, stirred, mixed, and partitioned.
    Journal of Statistical Software, 48
    DOI Software Methods

  • Kosmidis I, Firth D (2011). Multinomial logit bias reduction via the Poisson log-linear model.
    Biometrika, 98, 755-759
    DOI Theory Methods

  • Latuszynski K, Kosmidis I, Papaspiliopoulos O, Roberts G O (2011). Simulating events of unknown probabilities via reverse time martingales.
    Random Structures and Algorithms, 38 , 441-452
    DOI Methods

  • Kosmidis I, Firth D (2010). A generic algorithm for reducing bias in parametric estimation.
    Electronic Journal of Statistics, 4 1097-1112
    DOI R Code and an example Methods Theory

  • Kosmidis I, Firth D (2009). Bias reduction in exponential family nonlinear models.
    Biometrika, 96, 793-804
    DOI Theory

  • Kosmidis I (2008). The profileModel R package: Profiling objectives for models with linear predictors.
    R News, R Foundation for Statistical Computing, 8/2, 12-18.
    Link Software Methods

Selected presentations