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 (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 (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). Quantifying input-uncertainty in traffic assignment models. 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, December). Poisson mixture regression for Bayesian inference on large over-dispersed transportation origin-destination matrices. Presented at 27th International Workshop on Statistical Modelling, Prague
• A Bayesian approach for modeling origin-destination matrices
  Perrakis, K., Karlis, D., Cools, M., Janssens, D., & Wets, G. (2011, December). 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
  (2024, July). Proceedings of the 38th International Workshop on Statistical Modelling. Presented at 38th International Workshop on Statistical Modelling (IWSM), Durham, UK

Edited book

• Developments in Statistical Modelling
  Einbeck, J., Maeng, H., Ogundimu, E., & Perrakis, K. (Eds.). (2024). Developments in Statistical Modelling. Springer Nature. 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, 1-47
• 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