activity
20242026
most citedWhat Is a Good Imputation Under MAR Missingness?

2 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

stat.CO2026

Estimating Individual Customer Lifetime Values with R: The CLVTools Package

Markus Meierer, Patrick Bachmann, Jeffrey Näf +2

Customer lifetime value (CLV) describes a customer's long-term economic value for a business. This metric is widely used in marketing, for example, to select customers for a market…

stat.AP2026

A Practical Guide to Modern Imputation

Jeffrey Näf

This guide based on recent papers should help researchers avoid some of the most common pitfalls of missing value imputation imputation.

stat.CO2025

Do we Need Dozens of Methods for Real World Missing Value Imputation?

Krystyna Grzesiak, Christophe Muller, Julie Josse +1

Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various field…

stat.ME2025

How to rank imputation methods?

Jeffrey Näf, Krystyna Grzesiak, Erwan Scornet

Imputation is an attractive tool for dealing with the widespread issue of missing values. Consequently, studying and developing imputation methods has been an active field of resea…

stat.ME2025

Parametric MMD Estimation with Missing Values: Robustness to Missingness and Data Model Misspecification

Badr-Eddine Chérief-Abdellatif, Jeffrey Näf

In the missing data literature, the Maximum Likelihood Estimator (MLE) is celebrated for its ignorability property under missing at random (MAR) data. However, its sensitivity to m…

stat.ME2024

Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest

Jeffrey Näf, Junhyung Park, Herbert Susmann

The conditional average treatment effect (CATE) is a commonly targeted statistical parameter for measuring the effect of a treatment conditional on covariates. However, the CATE wi…

math.ST2024★ 2 cited

What Is a Good Imputation Under MAR Missingness?

Jeffrey Näf, Erwan Scornet, Julie Josse

Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various field…