| fdapace: Functional data analysis and empirical dynamics C Carroll, A Gajardo, Y Chen, X Dai, J Fan, PZ Hadjipantelis, K Han, H Ji, ... R package version 0.5 5, 2020 | 72 | 2020 |
| Additive functional regression for densities as responses K Han, HG Müller, BU Park Journal of the American Statistical Association, 2019 | 38 | 2019 |
| Smooth backfitting for errors-in-variables additive models K Han, BU Park The Annals of Statistics 46 (5), 2216-2250, 2018 | 29 | 2018 |
| Functional principal component analysis for identifying multivariate patterns and archetypes of growth, and their association with long-term cognitive development K Han, PZ Hadjipantelis, JL Wang, MS Kramer, S Yang, RM Martin, ... PLOS ONE 13 (11), e0207073, 2018 | 27 | 2018 |
| Smooth backfitting for additive modeling with small errors-in-variables, with an application to additive functional regression for multiple predictor functions K Han, HG Müller, BU Park Bernoulli 24 (2), 1233-1265, 2018 | 21 | 2018 |
| Two-phase analysis and study design for survival models with error-prone exposures K Han, T Lumley, BE Shepherd, PA Shaw Statistical Methods in Medical Research 30 (3), 857-874, 2021 | 14 | 2021 |
| Unbalanced sample size effect on genome-wide population differentiation studies K Han, KZ Kim, JM Oh, IW Kim, K Kim, T Park International journal of data mining and bioinformatics 6 (5), 490-504, 2012 | 12 | 2012 |
| Combining multiple imputation with raking of weights: An efficient and robust approach in the setting of nearly true models K Han, PA Shaw, T Lumley Statistics in Medicine 40 (30), 6777-6791, 2021 | 10 | 2021 |
| Estimation of errors-in-variables partially linear additive models ER Lee, K Han, BU Park Statistica Sinica 28 (4), 2353-2373, 2018 | 8 | 2018 |
| Multiwave validation sampling for error-prone electronic health records BE Shepherd, K Han, T Chen, A Bian, S Pugh, SN Duda, T Lumley, ... Biometrics 79 (3), 2649-2663, 2023 | 5 | 2023 |
| Analysis of Error-prone Electronic Health Records with Multi-wave Validation Sampling: Association of Maternal Weight Gain during Pregnancy with Childhood Outcomes BE Shepherd, K Han, T Chen, A Bian, S Pugh, SN Duda, T Lumley, ... arXiv preprint arXiv:2109.14001, 2021 | 2 | 2021 |
| Functional linear regression for functional response via sparse basis selection K Han, H Shin Journal of the Korean Statistical Society 44 (3), 376-389, 2015 | 2 | 2015 |
| Smooth backfitting for errors-in-variables varying coefficient regression models K Han, YK Lee, BU Park Computational Statistics & Data Analysis 145, 106909, 2020 | 1 | 2020 |
| Associating Growth in Infancy and Cognitive Performance in Early Childhood: A functional data analysis approach PZ Hadjipantelis, K Han, JL Wang, S Yang, RM Martin, MS Kramer, ... arXiv preprint arXiv:1808.01384, 2018 | 1 | 2018 |
| Errors-in-variables Fr\'echet Regression with Low-rank Covariate Approximation D Song, K Han Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Associations Between Gestational Weight Gain, Gestational Diabetes, and Childhood Obesity Incidence NM Sneed, WJ Heerman, PA Shaw, K Han, T Chen, A Bian, S Pugh, ... Maternal and Child Health Journal, 1-10, 2023 | | 2023 |
| Testing linear operator constraints in functional response regression with incomplete response functions Y Park, K Han, DG Simpson Electronic Journal of Statistics 17 (2), 3143-3180, 2023 | | 2023 |
