| On the use of marginal posteriors in marginal likelihood estimation via importance sampling K Perrakis, I Ntzoufras, EG Tsionas Computational Statistics & Data Analysis 77, 54-69, 2014 | 87 | 2014 |
| Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics S Warnat-Herresthal, K Perrakis, B Taschler, M Becker, K Baßler, M Beyer, ... Iscience 23 (1), 2020 | 68 | 2020 |
| A Bayesian approach for modeling origin–destination matrices K Perrakis, D Karlis, M Cools, D Janssens, K Vanhoof, G Wets Transportation Research Part A: Policy and Practice 46 (1), 200-212, 2012 | 63 | 2012 |
| Controlling for seasonal patterns and time varying confounders in time‐series epidemiological models: a simulation study K Perrakis, A Gryparis, J Schwartz, AL Tertre, K Katsouyanni, F Forastiere, ... Statistics in Medicine 33 (28), 4904-4918, 2014 | 24 | 2014 |
| Power-expected-posterior priors for generalized linear models D Fouskakis, I Ntzoufras, K Perrakis | 23 | 2018 |
| Bayesian inference for transportation origin–destination matrices: the Poisson–inverse Gaussian and other Poisson mixtures K Perrakis, D Karlis, M Cools, D Janssens Journal of the Royal Statistical Society Series A: Statistics in Society 178 …, 2015 | 21 | 2015 |
| Bayesian variable selection using the hyper‐g prior in WinBUGS K Perrakis, I Ntzoufras Wiley Interdisciplinary Reviews: Computational Statistics 10 (6), e1442, 2018 | 11 | 2018 |
| Stochastic search variable selection (SSVS) K Perrakis, I Ntzoufras Wiley StatsRef: Statistics Reference Online, 1-6, 2015 | 8 | 2015 |
| Scalable prediction of acute myeloid leukemia using high‑dimensional machine learning and blood transcriptomics. Iscience. 2020 S Warnat-Herresthal, K Perrakis, B Taschler, M Becker, K Baßler, M Beyer, ... This paper underscores the immense potential of machine learning in …, 2019 | 7 | 2019 |
| Scalable Bayesian regression in high dimensions with multiple data sources K Perrakis, S Mukherjee, Alzheimer’s Disease Neuroimaging Initiative Journal of Computational and Graphical Statistics 29 (1), 28-39, 2020 | 6 | 2020 |
| Latent group structure and regularized regression K Perrakis, T Lartigue, F Dondelinger, S Mukherjee | 4 | 2020 |
| Variations of power-expected-posterior priors in normal regression models D Fouskakis, I Ntzoufras, K Perrakis Computational Statistics & Data Analysis 143, 106836, 2020 | 3 | 2020 |
| Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics. iScience S Warnat-Herresthal, K Perrakis, B Taschler, M Becker, K Baßler, M Beyer | 3 | 2020 |
| Regularized joint mixture models K Perrakis, T Lartigue, F Dondelinger, S Mukherjee Journal of Machine Learning Research 24 (19), 1-47, 2023 | 2 | 2023 |
| Diagnostic value of blood gene expression-based classifiers as exemplified for acute myeloid leukemia S Warnat-Herresthal, K Perrakis, B Taschler, M Becker, L Seep, K Baßler, ... bioRxiv, 382143, 2018 | 2 | 2018 |
| Bayesian variable selection for generalized linear models using the power-conditional-expected-posterior prior K Perrakis, D Fouskakis, I Ntzoufras Bayesian Statistics from Methods to Models and Applications: Research from …, 2015 | 2 | 2015 |
| Quantifying Input Uncertainty in Traffic Assignment Models K Perrakis, M Cools, D Karlis, D Janssens, B Kochan, T Bellemans, ... 91st Annual Meeting of the Transportation Research Board, 2012 | 2 | 2012 |
| Bayesian Cluster Weighted Gaussian Models P Papastamoulis, K Perrakis arXiv preprint arXiv:2411.18957, 2024 | | 2024 |
| Penalized longitudinal mixed models with latent group structure, with an application in neurodegenerative diseases F Hatami, K Perrakis, J Cooper-Knock, S Mukherjee, F Dondelinger medRxiv, 2020.11. 10.20229302, 2020 | | 2020 |
| Bayesian Analysis MA Terres, M Fuentes, D Hesterberg, M Polizzotto, D Durante, ... Bayesian Analysis 13 (1), 2018 | | 2018 |
