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:
- Turing Interest Group on Data science for sports, activity, and well-being (founding member)
- Turing Interest Group on Online machine learning (member)
- EPICentre research group (member; 2014-2017)
- Statistics in Sports and Health research group (founder and leader; 2014-2017)
- Statistics for Health Economic Evaluation research group (member; 2015-2017)
- General Theory and Methodology and Computational Statistics research themes at the Department of Statistical Science, UCL (member; 2010-2017)
Preprints and other unpublished work
- Kosmidis I, Kenne Pagui E C and Sartori N (2018). Mean and median bias reduction in generalized linear models.
ArXiV Methods Theory
- Bartlett T E, Kosmidis I and Silva R (2018). Two-way sparsity for time-varying networks with applications in genomics.
ArXiv Methods Applications
- Di Caterina C and Kosmidis I (2017). Location-adjusted Wald statistic for scalar parameters.
ArXiv Methods Theory
- 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
- 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.
To appear in Bulletin of Earthquake Engineering
- Tsokos A, Narayanan S, Kosmidis I, Baio G, Cucuringu M, Whitaker G and Király F J (2018). Modeling outcomes of soccer matches.
To appear in Machine Learning
DOI ArXiV Applications
- Kyriakou S, Kosmidis I and Sartori N (2018). Median bias reduction in random-effects meta-analysis and meta-regression.
To appear in Statistical Methods in Medical Research
DOI ArXiV Methods 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
- 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
- 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
- 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)
- 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