Interests

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:

• Turing Interest Group on Data Science for Sports, Activity, and Well-being (founding member, organizer; 2017-2022)

• Turing Interest Group on Machine Learning by Systems, for Systems (member; 2019-2020)

• Turing Interest Group on Online Machine Learning (member; 2017-2020)

• Statistics in Sports and Health research group (founder and leader; 2015-2017)

• Statistics for Health Economic Evaluation research group (member; 2015-2017)

• EPICentre research group (member; 2014-2017)

• General Theory and Methodology and Computational Statistics research themes at the Department of Statistical Science, UCL (member; 2010-2017)

Preprints and unpublished reports

• Sterzinger P, Kosmidis I, Moustaki I (2025). Maximum softly penalised likelihood in factor analysis.
  ArXiV Supplementary material Theory Methods Applications

• Jamil H, Rosseel Y, Kemp O, Kosmidis I (2025). Bias-reduced estimation of structural equation models.
  ArXiV Supplementary material Theory Methods

• Sterzinger P, Kosmidis I (2024). Diaconis-Ylvisaker prior penalized likelihood for \(p /n \to \kappa \in (0, 1)\) logistic regression.
  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

Publications

• Kosmidis I, Zeileis A (2025). Extended-support beta regression for \([0, 1]\) responses.
  Journal of the Royal Statistical Society: Series C (in press)
  DOI ArXiV Supplementary material Methods Applications

• Kosmidis I, Zietkiewicz P (2025). Jeffreys-prior penalty for high-dimensional logistic regression: A conjecture about aggregate bias.
  Statistical Science (accepted)
  ArXiV Supplementary material Theory Methods

• Košuta T, Heinze G, Kastrin A, Kosmidis I, Blagus R (2025). The impact of bias due to exponentiation in the estimation of hazard, risk, and odds ratios: An empirical investigation from 1,495,059 effect sizes from MEDLINE/PubMed abstracts.
  BMC Medical Research Methodology, 25, 109.
  DOI Methods Applications

• Zietkiewicz P, Kosmidis I (2024). Bounded-memory adjusted scores estimation in generalized linear models with large data sets.
  Statistics and Computing, 34, 138.
  DOI ArXiV Supplementary material Theory Methods

• Wang Z, Dellaportas P, Kosmidis I (2024). Bayesian Tensor Factorisations for Time Series of Counts.
  Machine Learning, 113, 3731-3750
  The 15th Asian Conference on Machine Learning (ACML 2023)
  DOI ArXiV ACML Theory Methods

• Kosmidis I, Lunardon N (2024). Empirical bias-reducing adjustments to estimating functions.
  Journal of the Royal Statistical Society: Series B. 86, 62–89.
  DOI ArXiV Supplementary material Theory Methods

• Kosmidis I (2023). Mean and median bias reduction: A concise review and application to adjacent-categories logit models.
  In: Kateri, M., Moustaki, I. (eds) Trends and Challenges in Categorical Data Analysis. Statistics for Social and Behavioral Sciences. Springer, Cham.
  DOI ArXiV Supplementary material Methods Applications

• Panos, A, Kosmidis I, Dellaportas P (2023). Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences.
  In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), PMLR 206:236-252
  PMLR ArXiV Supplementary material Methods Applications

• Narayanan S, Kosmidis I, Dellaportas P (2023). Flexible marked spatio-temporal point processes with applications to event sequences from association football.
  Journal of the Royal Statistical Society: Series C, 72, 1095–1126 (with discussion).
  DOI ArXiV Methods Applications
  DOI: D Karlis (Proposer) DOI: L Egidi (Seconder) DOI: M Stival, L Schiavon DOI: J Mateu DOI: P Smith DOI: R Yurko, R Nugent DOI: Authors’ reply

• Sterzinger P, Kosmidis I (2023). Maximum softly-penalized likelihood for mixed effects logistic regression.
  Statisics and Computing, 33, 53
  DOI ArXiV Supplementary material Methods Applications

• Bellio R, Kosmidis I, Salvan A, Sartori N (2023). Parametric bootstrap inference for stratified models with high-dimensional nuisance specifications.
  Statistica Sinica, 33
  DOI ArXiV Theory Methods

• Bartlett T E, Kosmidis I, Silva R (2021). Two-way sparsity for time-varying networks with applications in genomics.
  Annals of Applied Statistics, 15, 856-879
  DOI ArXiv Methods Applications

• 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

• Turner H L, van Etten J, Firth D, Kosmidis I (2020). Modelling rankings in R: the PlackettLuce package.
  Computational Statistics, 35, 1027–1057
  DOI ArXiV Software Methods

• 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

PhD thesis

• Kosmidis I (2007). Bias reduction in exponential family nonlinear models (errata)