# 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# Preprints and unpublished reports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**(2022). Maximum softly-penalized likelihood for mixed effects logistic regression.

ArXiV Supplementary material Methods Applications**Kosmidis I**(2021). Mean and median bias reduction: A concise review and application to adjacent-categories logit models.

ArXiV Supplementary material Methods ApplicationsKindalova 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 ApplicationsPanos, A,

**Kosmidis I**, Dellaportas P (2021). Scalable and Interpretable Marked Point Processes.

ArXiV Methods ApplicationsKöll, 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**Kosmidis I**, Lunardon N (2021). Empirical bias-reducing adjustments to estimating functions.

ArXiV Supplementary material Theory MethodsFirth D,

**Kosmidis I**, Turner H L (2019). Davidson-Luce model for multi-item choice with ties.

ArXiV TheoryKarimalis 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

Narayanan S,

**Kosmidis I**, Dellaportas P (2022+). Flexible marked spatio-temporal point processes with applications to event sequences from association football.

*Discussion paper in the Journal of the Royal Statistical Society: Series C (Accepted)*

ArXiV Methods ApplicationsBellio R,

**Kosmidis I**, Salvan A, Sartori N (2022+). Parametric bootstrap inference for stratified models with high-dimensional nuisance specifications.

*Statistica Sinica*(forthcoming)

DOI ArXiV Theory MethodsBartlett 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 TheoryKyriakou 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 ApplicationsTsokos 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 ApplicationsIoannou 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 ApplicationsFrick 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 ApplicationsMaqsood 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 ApplicationsPanayi 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 ApplicationsAmes 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 MethodsGrün 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 MethodsLatuszynski 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)

# Selected presentations

- 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