Staff profile

• Bayesian statistics
• Model and variable selection
• High-dimensional regression
• Statistical modelling

Chapter in book

• Stochastic Search Variable Selection (SSVS)
  
  Perrakis, K., & Ntzoufras, I. (2015). Stochastic Search Variable Selection (SSVS). In N. Balakrishnan, P. Brandimarte, B. Everitt, G. Molenberghs, W. Piegorsch, & F. Ruggeri (Eds.), Wiley StatsRef: Statistics Reference Online. (pp. 1-6). John Wiley and Sons. https://doi.org/10.1002/9781118445112.stat07829
• Bayesian Variable Selection for Generalized Linear Models Using the Power-Conditional-Expected-Posterior Prior
  
  Perrakis, K., Fouskakis, D., & Ntzoufras, I. (2015). Bayesian Variable Selection for Generalized Linear Models Using the Power-Conditional-Expected-Posterior Prior. In S. Frühwirth-Schnatter, A. Bitto, G. Kastner, & A. Posekany (Eds.), Bayesian Statistics from Methods to Models and Applications. (pp. 59-73). Springer Proceedings in Mathematics & Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-16238-6_6

Conference Paper

• Quantifying input-uncertainty in traffic assignment models
  
  Perrakis, K., Cools, M., Karlis, D., Janssens, D., Kochan, B., Bellemans, T., & Wets, G. (2012, January 22 – 2012, January 26). Quantifying input-uncertainty in traffic assignment models [Conference paper]. Presented at Transportation Research Board 91st Annual Meeting, Washington, DC, United States.
• Poisson mixture regression for Bayesian inference on large over-dispersed transportation origin-destination matrices
  
  Perrakis, K., Karlis, D., Cools, M., Janssens, D., & Wets, G. (2012). Poisson mixture regression for Bayesian inference on large over-dispersed transportation origin-destination matrices (A. Komarek & S. Nagy, Eds.). Statistical Modelling Society.
• A Bayesian approach for modeling origin-destination matrices
  
  Perrakis, K., Karlis, D., Cools, M., Janssens, D., & Wets, G. (2011). A Bayesian approach for modeling origin-destination matrices. Presented at Transportation Research Board 90th Annual Meeting, Washington, DC, United States.

Conference Proceeding

• Proceedings of the 38th International Workshop on Statistical Modelling
  
  Einbeck, J., Drikvandi, R., Karagiannis, G., Perrakis, K., & Zhang, Q. (Eds.). (2024). Proceedings of the 38th International Workshop on Statistical Modelling. Durham University.

Edited book

• Developments in Statistical Modelling
  
  Einbeck, J., Maeng, H., Ogundimu, E., & Perrakis, K. (Eds.). (2024). Developments in Statistical Modelling. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-65723-8

Journal Article

• Regularized joint mixture models
  
  Perrakis, K., Lartigue, T., Dondelinger, F., & Mukherjee, S. (2023). Regularized joint mixture models. Journal of Machine Learning Research, 24, Article 19.
• Variations of power-expected-posterior priors in normal regression models
  
  Fouskakis, D., Ntzoufras, I., & Perrakis, K. (2020). Variations of power-expected-posterior priors in normal regression models. Computational Statistics & Data Analysis, 143, Article 106836. https://doi.org/10.1016/j.csda.2019.106836
• Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics
  
  Warnat-Herresthal, S., Perrakis, K., Taschler, B., Becker, M., Baßler, K., Beyer, M., Günther, P., Schulte-Schrepping, J., Seep, L., Klee, K., Ulas, T., Haferlach, T., Mukherjee, S., & Schultze, J. L. (2020). Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics. IScience, 23(1), Article 100780. https://doi.org/10.1016/j.isci.2019.100780
• Scalable Bayesian regression in high dimensions with multiple data sources
  
  Perrakis, K., Mukherjee, S., & Initiative, T. A. D. N. (2020). Scalable Bayesian regression in high dimensions with multiple data sources. Journal of Computational and Graphical Statistics, 29(1), 28-39. https://doi.org/10.1080/10618600.2019.1624294
• Bayesian variable selection using the hyper-g prior in WinBUGS
  
  Perrakis, K., & Ntzoufras, I. (2018). Bayesian variable selection using the hyper-g prior in WinBUGS. Wiley Interdisciplinary Reviews: Computational Statistics, 10(6). https://doi.org/10.1002/wics.1442
• Power-expected-posterior priors for generalized linear models
  
  Fouskakis, D., Ntzoufras, I., & Perrakis, K. (2017). Power-expected-posterior priors for generalized linear models. Bayesian Analysis, 13(3), 721-748. https://doi.org/10.1214/17-ba1066
• Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures
  
  Perrakis, K., Karlis, D., Cools, M., & Janssens, D. (2015). Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures. Journal of the Royal Statistical Society: Series A, 178(1), 271-296. https://doi.org/10.1111/rssa.12057
• On the use of marginal posteriors in marginal likelihood estimation via importance sampling
  
  Perrakis, K., Ntzoufras, I., & Tsionas, E. G. (2014). On the use of marginal posteriors in marginal likelihood estimation via importance sampling. Computational Statistics & Data Analysis, 77, 54-69. https://doi.org/10.1016/j.csda.2014.03.004
• Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study
  
  Perrakis, K., Gryparis, A., Schwartz, J., Tertre, A. L., Katsouyanni, K., Forastiere, F., Stafoggia, M., & Samoli, E. (2014). Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study. Statistics in Medicine, 33(28), 4904-4918. https://doi.org/10.1002/sim.6271
• A Bayesian approach for modeling origin–destination matrices
  
  Perrakis, K., Karlis, D., Cools, M., Janssens, D., Vanhoof, K., & Wets, G. (2012). A Bayesian approach for modeling origin–destination matrices. Transportation Research Part A: Policy and Practice, 46(1), 200-212. https://doi.org/10.1016/j.tra.2011.06.005