5 citations · 9 across the 10 of their papers we have counts for
11 papers
Asymptotics of Nonparametric Estimation under General Non-monotone MAR Missingness: A Nonparametric Maximum Likelihood Approach
Yating Zou, Huimin Hu, Jeffrey Näf
Missing data constitute a pervasive challenge in empirical research. Consequently, there is an ever-growing number of methods designed to address this challenge, with multiple impu…
Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
Gitte Kremling, Jeffrey Näf, Johannes Lederer
The prevalence of missing values in data science poses a substantial risk to any further analyses. Despite a wealth of research, principled nonparametric methods to deal with gener…
CLVAE: A Variational Autoencoder for Long-Term Customer Revenue Forecasting
Jeffrey Näf, Riana Valera Mbelson, Markus Meierer
Predicting customers' long-term revenue from sparse and irregular transaction data is central to marketing resource allocation in non-contractual settings, yet existing approaches…
MMD-based Variable Importance for Distributional Random Forest
Clément Bénard, Jeffrey Näf, Julie Josse
Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables. In th…
Confidence and Uncertainty Assessment for Distributional Random Forests
Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann +1
The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions. Due to its general estimation procedure,…
R-NL: Covariance Matrix Estimation for Elliptical Distributions based on Nonlinear Shrinkage
Simon Hediger, Jeffrey Näf, Michael Wolf
We combine Tyler's robust estimator of the dispersion matrix with nonlinear shrinkage. This approach delivers a simple and fast estimator of the dispersion matrix in elliptical mod…
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…
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…