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-)
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)
Preprints and unpublished reports
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 (2010). On iterative adjustment of responses for the reduction of bias in binary regression models.
CRiSM Working Paper Series Methods Theory
- Bartlett T E, Kosmidis I, Silva R (2020). Two-way sparsity for time-varying networks with applications in genomics.
Annals of Applied Statistics (forthcoming)
DOI ArXiv Methods Applications
- Whitaker G A, Silva R, Edwards D, Kosmidis I (2020). A Bayesian inference approach for determining player abilities in football.
Journal of the Royal Statistical Society: Series C (forthcoming)
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.
DOI BioRxiV Methods Applications
- Kosmidis I, Firth D (2020). Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models.
Biometrika (to appear)
DOI ArXiV Supplementary material Theory Methods
- 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
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
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
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
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 (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
- Kosmidis I (2007). Bias reduction in exponential family nonlinear models (errata)
- Package and collaboration networks in CRAN. Oxford R User Group, Oxford, UK, November 2018
ikosmidis_cranly_OxfordRUG_2018.R has the R code used in the presentation (note that outputs may vary as CRAN changes)
- Location-adjusted Wald statistics. Institute for Statistics and Mathematics, WU Wien, Vienna, Austria, May 2018
- Reduced-bias estimation for models with ordinal responses. CEN ISBS 2017 Joint Conference, Vienna, Austria, August 2017
- Reduced-bias inference for multi-dimensional Rasch models with applications. 28th International Workshop on Statistical Modelling, Palermo, Italy, July 2013
- Bias reduction in generalized nonlinear models. Joint Statistical Meetings 2009, Washington, DC, 2009
- Profiling the parameters of models with linear predictors. useR 2008, Dortmund, Germany, August 2008