5 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
Christophe Muller, Erwan Scornet, Julie Josse
Predicting with missing inputs challenges even parametric models, as parameter estimation alone is insufficient for prediction on incomplete data. While several works study predict…
Predictive Uncertainty Quantification with Missing Covariates
Margaux Zaffran, Julie Josse, Yaniv Romano +1
Predictive uncertainty quantification is crucial in decision-making problems. We investigate how to adequately quantify predictive uncertainty with missing covariates. A bottleneck…
Logistic Regression with Missing Covariates -- Parameter Estimation, Model Selection and Prediction within a Joint-Modeling Framework
Wei Jiang, Julie Josse, Marc Lavielle +1
Logistic regression is a common classification method in supervised learning. Surprisingly, there are very few solutions for performing logistic regression with missing values in t…