| Epistemic confidence in the observed confidence interval Y Pawitan, H Lee, Y Lee Scandinavian Journal of Statistics, 2023 | 4 | 2023 |
| H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features H Lee, Y Lee The 40th International Conference on Machine Learning, 18974-18987, 2023 | 3 | 2023 |
| Albatross analytics a hands-on into practice: statistical and data science application RE Caraka, Y Lee, J Han, H Lee, M Noh, I Do Ha, PU Gio, B Pardamean Journal of Big Data 9 (1), 70, 2022 | 3 | 2022 |
| Penalized variable selection for cause‐specific hazard frailty models with clustered competing‐risks data TW Rakhmawati, ID Ha, H Lee, Y Lee Statistics in Medicine 40 (29), 6541-6557, 2021 | 3 | 2021 |
| Epistemic confidence, the Dutch Book and relevant subsets Y Pawitan, H Lee, Y Lee arXiv preprint arXiv:2104.14712, 2021 | 2 | 2021 |
| Subject-specific Deep Neural Networks for Count Data with High-cardinality Categorical Features H Lee, ID Ha, C Hwang, Y Lee arXiv preprint arXiv:2310.11654, 2023 | 1 | 2023 |
| Deep Neural Networks for Semiparametric Frailty Models via H-likelihood H Lee, IL HA, Y Lee arXiv preprint arXiv:2307.06581, 2023 | 1 | 2023 |
| On the Statistical Foundations of H-likelihood for Unobserved Random Variables H Lee, J Han, Y Lee arXiv preprint arXiv:2310.09955, 2023 | | 2023 |
| Point Mass in the Confidence Distribution: Is it a Drawback or an Advantage? H Lee, Y Lee arXiv preprint arXiv:2310.09960, 2023 | | 2023 |
