2 citations · 4 across the 5 of their papers we have counts for
6 papers
PKLM: A flexible MCAR test using Classification
Meta-Lina Spohn, Jeffrey Näf, Loris Michel +1
We develop a fully non-parametric, easy-to-use, and powerful test for the missing completely at random (MCAR) assumption on the missingness mechanism of a dataset. The test compare…
Imputation Scores
Jeffrey Näf, Meta-Lina Spohn, Loris Michel +1
Given the prevalence of missing data in modern statistical research, a broad range of methods is available for any given imputation task. How does one choose the `best' imputation…
Solving optimal stopping problems with Deep Q-Learning
John Ery, Loris Michel
We propose a reinforcement learning (RL) approach to model optimal exercise strategies for option-type products. We pursue the RL avenue in order to learn the optimal action-value…
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
Domagoj Ćevid, Loris Michel, Jeffrey Näf +2
Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm. Part of its appeal and reason for its versatility is its (implicit) construct…
High Probability Lower Bounds for the Total Variation Distance
Loris Michel, Jeffrey Näf, Nicolai Meinshausen
The statistics and machine learning communities have recently seen a growing interest in classification-based approaches to two-sample testing. The outcome of a classification-base…
On the Use of Random Forest for Two-Sample Testing
Simon Hediger, Loris Michel, Jeffrey Näf
Following the line of classification-based two-sample testing, tests based on the Random Forest classifier are proposed. The developed tests are easy to use, require almost no tuni…