| On the use of random forest for two-sample testing S Hediger, L Michel, J Näf Computational Statistics & Data Analysis 170, 107435, 2022 | 42 | 2022 |
| Distributional random forests: Heterogeneity adjustment and multivariate distributional regression D Cevid, L Michel, J Näf, P Bühlmann, N Meinshausen Journal of Machine Learning Research 23 (333), 1-79, 2022 | 32 | 2022 |
| Heterogeneous tail generalized COMFORT modeling via Cholesky decomposition J Näf, MS Paolella, P Polak Journal of Multivariate Analysis 172, 84-106, 2019 | 17 | 2019 |
| The role of time-varying contextual factors in latent attrition models for customer base analysis P Bachmann, M Meierer, J Näf Marketing Science 40 (4), 783-809, 2021 | 11 | 2021 |
| PKLM: A flexible MCAR test using Classification ML Spohn, J Näf, L Michel, N Meinshausen arXiv preprint arXiv:2109.10150, 2021 | 5 | 2021 |
| Heterogeneous tail generalized common factor modeling S Hediger, J Näf, MS Paolella, P Polak Swiss Finance Institute Research Paper, 2021 | 4 | 2021 |
| Imputation scores J Näf, ML Spohn, L Michel, N Meinshausen The Annals of Applied Statistics 17 (3), 2452-2472, 2023 | 2 | 2023 |
| Heterogeneous tail generalized common factor modeling S Hediger, J Näf, MS Paolella, P Polak Digital Finance 5 (2), 389-420, 2023 | 2 | 2023 |
| Confidence and uncertainty assessment for distributional random forests J Näf, C Emmenegger, P Bühlmann, N Meinshausen Journal of Machine Learning Research 24 (366), 1-77, 2023 | 2 | 2023 |
| Shrinking in COMFORT S Hediger, J Näf SSRN, 2022 | 2 | 2022 |
| High probability lower bounds for the total variation distance L Michel, J Näf, N Meinshausen arXiv preprint arXiv:2005.06006, 2020 | 2 | 2020 |
| Combining the MGHyp distribution with nonlinear shrinkage in modeling financial asset returns S Hediger, J Näf Journal of Empirical Finance 77, 101489, 2024 | 1 | 2024 |
| MMD-based Variable Importance for Distributional Random Forest C Bénard, J Näf, J Josse International Conference on Artificial Intelligence and Statistics, 1324-1332, 2024 | | 2024 |
| What Is a Good Imputation Under MAR Missingness? J Näf, J Josse arXiv preprint arXiv:2403.19196, 2024 | | 2024 |
| R-NL: Covariance Matrix Estimation for Elliptical Distributions based on Nonlinear Shrinkage S Hediger, J Näf, M Wolf IEEE Transactions on Signal Processing, 2023 | | 2023 |
| Distributional Random Forests J Näf | | 2023 |
| Distributional Prediction, Missing Values, and Tree Ensembles J Näf ETH Zurich, 2023 | | 2023 |
| Not on Every Day Your Average Joe: Extending Probabilistic Modeling of Customers' Spending Behavior P Bachmann, M Meierer, J Näf Theory+ Practice in Marketing 2022, 2022 | | 2022 |
| Package ‘CLVTools’ C SystemRequirements | | 2020 |
| Estimating Individual Customer Lifetime Values with R: The CLVTools Package P Bachmann, M Meierer, J Näf, P Schilter, R Algesheimer Interactive Marketing 23 (1), 61-69, 0 | | |
