My current interests are broadly on

  • Penalized and pseudo likelihood theory and methods
  • Statistical computing and algorithms for regression problems
  • Methods for clustering
  • Scientific software development

I also engage in cross-disciplinary work, focusing on data-analytic settings in sports science (uncovering the links between human behaviour, health, fitness and overall well-being), finance (modelling the dynamics of financial indicators with structural dependencies), and earthquake engineering (assessment of the vulnerability of the built environment from post-hazard survey data).

Research groups and themes

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

Preprints and other unpublished work

  • Kosmidis I, Firth D (2019). Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models.
    ArXiV Supplementary material Theory Methods
  • Turner H L, van Etten J, Firth D, Kosmidis I (2018). Modelling rankings in R: the PlackettLuce package.
    ArXiV Software Methods
  • Bartlett T E, Kosmidis I and Silva R (2018). Two-way sparsity for time-varying networks with applications in genomics.
    ArXiv Methods Applications
  • Karimalis E, Kosmidis I and 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 and Passfield L (2015). Linking the performance of endurance runners to training and physiological effects via multi-resolution elastic net.
    ArXiV Applications Methods
  • Kosmidis I and 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


  • Kosmidis I, Kenne Pagui E C and Sartori N (2019). Mean and median bias reduction in generalized linear models.
    To appear in Statistics and Computing
    DOI ArXiV Methods Theory

  • Di Caterina C and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and Kosmidis I (2015). Upside and downside risk exposures of currency carry trades via tail dependence.
    In: Glau, M. Scherer, and R. Zagst (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 Theory Methods
  • Grün B, Kosmidis I and Zeileis A (2012). Extended Beta regression in R: Shaken, stirred, mixed, and partitioned.
    Journal of Statistical Software, 48
    DOI Software Methods
  • Kosmidis I and 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 and Roberts G O (2011). Simulating events of unknown probabilities via reverse time martingales.
    Random Structures and Algorithms, 38 , 441-452
    DOI Methods
  • Kosmidis I and 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 and 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