# Interests

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 Machine Learning by systems, for systems (founding member)
- 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# Preprints and other unpublished work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**, Firth D (2018). Jeffreys’ prior, 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 - 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

# Publications

**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- 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 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

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

# 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