Asymptotic Unbiasedness of the Permutation Importance Measure in Random Forest Models. - Burim Ramosaj; Markus Pauly (CoRR, 2019)

Predicting missing values: a comparative study on non-parametric approaches for imputation. - Burim Ramosaj; Markus Pauly (Comput. Stat., 2019)

A cautionary tale on using imputation methods for inference in matched-pairs design. - Burim Ramosaj; Lubna Amro; Markus Pauly (Bioinform., 2020)

Universal Approximation Theorems of Fully Connected Binarized Neural Networks. - Mikail Yayla; Mario Günzel; Burim Ramosaj; Jian-Jia Chen (CoRR, 2021)

Interpretable Machines: Constructing Valid Prediction Intervals with Random Forests. - Burim Ramosaj (CoRR, 2021)

On the Relation between Prediction and Imputation Accuracy under Missing Covariates. - Burim Ramosaj; Justus Tulowietzki; Markus Pauly (Entropy, 2022)

Enhancing endangered species monitoring by lowering data entry requirements with imputation techniques as a preprocessing step for the footprint identification technology (FIT). - Frederick Kistner; Justus Tulowietzki; Larissa Slaney; Sky K. Alibhai; Zoe C. Jewell; Burim Ramosaj; Markus Pauly (Ecol. Informatics, 2024)