- Bias in parametric estimation: reduction and useful side-effects. Kosmidis, I. (2014). WIRE Computational Statistics, 6, 185-196 (also available at ArXiv e-prints, arXiv:1311.6311).
- Improved estimation in cumulative link models. Kosmidis, I. (2014). Journal of the Royal Statistical Society: Series B, 76, 169-196 (also available at ArXiv e-prints, arXiv:1204.0105).
- Extended Beta regression in R: Shaken, stirred, mixed, and partitioned. Grün, B., Kosmidis, I. and Zeileis, A. (2012). Journal of Statistical Software, 48, 11.
- Multinomial logit bias reduction via the Poisson log-linear model. Kosmidis, I., Firth, D. (2011). Biometrika, 98, 755-759.
- Simulating events of unknown probabilities via reverse time martingales. Latuszynski, K., Kosmidis, I., Papaspiliopoulos, O. and Roberts, G. O. (2011). Random Structures and Algorithms, 38, 441-452.
- A generic algorithm for reducing bias in parametric estimation. Kosmidis, I., Firth, D. (2010). Electronic Journal of Statistics, 4, 1097-1112. Relevant R Code and an example are available here .
- Bias reduction in exponential family nonlinear models. Kosmidis, I., Firth, D. (2009). Biometrika, 96, 793-804.
- The profileModel R package: Profiling objectives for models with linear predictors. Kosmidis, I. (2008). R News, 8/2, 12-18, R Foundation for Statistical Computing.
Book chapters and proceedings
- Upside and downside risk exposures of currency carry trades via tail dependence. Ames, M., Peters, G. W., Bagnarosa, G. and Kosmidis, I. (2015). In: Glau, M. Scherer, and R. Zagst (Eds.), Innovations in Quantitative Risk Management, Volume 99 of Springer Proceedings in Mathematics Statistics, pp. 163-181. (also available at ArXiv e-prints, arXiv:1406.4322).
- Linking the performance of endurance runners to training and physiological effects via multi-resolution elastic net. Kosmidis, I. and Passfield, L. (2015). ArXiv e-prints, arXiv:1506.01388.
- Model-based clustering using copulas with applications. Kosmidis, I. and Karlis, D. (2014). ArXiv e-prints, arXiv:1404.4077.
- Liquidity commonality does not imply liquidity resilience commonality: A functional characterisation for ultra-high frequency cross-sectional LOB data. Panayi, E., Peters, G. W., Kosmidis, I. (2014). ArXiv e-prints, arXiv:1406.5486.
- Supervised sampling for clustering large data sets. Kosmidis, I., Karlis, D. (2010). CRiSM working paper 10-10.
- On iterative adjustment of responses for the reduction of bias in binary regression models. Kosmidis, I. (2009). CRiSM working paper 09-36.
- Bias reduction in exponential family nonlinear models (August 2007, Department of Statistics, University of Warwick)
- Reduced-bias inference for multi-dimensional Rasch models with applications . 28th International Workshop on Statistical Modelling, Palermo, Italy, July 2013.
- brglm: Bias reduction in generalized linear models . useR! 2011, Coventry, UK, August 2011.
- Bias reduction in generalized nonlinear models . Joint Statistical Meetings 2009, Washington, DC, 2009.
- On iterative adjustment of the responses for the reduction of bias in binary regression models . 24th International Workshop on Statistical Modelling , Ithaka, NY, July 2009.
- Reduction of bias in exponential family models with emphasis on models for categorical responses . University of Padua, Italy, May 2009.
- Profiling the parameters of models with linear predictors . useR 2008, Dortmund, Germany, August 2008.
Below is a list of topics that I am highly interested in and I am/would be keen to research collaborating with a PhD student. Feel free to contact me if you are interested on any of those. Information on the application process and on available funding opportunities can be found here.
Optimal estimation and inference
- Inference for models with many nuisance parameters (in progress)
- The bias of functional estimators
- Improved estimation for generalized linear mixed effects models (in progress)
Inferece from big data sets
- Estimation and inference in generalized linear models from big data sets
Mixture models and applications
- Model-based image segmentation with covariates
Applications in Sports
- Statistical models for high-frequency health and fitness data in sports (in progress)
- Injury prediction and prevention in Sports (in progress)
The above list is in no case exhaustive; if you have something else in mind and you are interested in working with me I would be more than happy to discuss.