5 citations · 9 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…