arXiv:2607. 07767v1 Announce Type: cross Abstract: Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametric assumptions that ignore the joint data distribution.
By Andrea Basteri, Carlo Ciliberto, Alessandro Rudi
Missing data are a fundamental challenge in statistical analysis and machine learning, as the choice of imputation method substantially impacts downstream inference. In this work, we propose two hybri...
The paper introduces two hybrid imputation methods, NuclearForest and SoftForest, that combine a low‑rank initialization (using Singular Value Thresholding or SoftImpute) with a single Random Forest refinement. These methods capture global covariance patterns and local nonlinear dependencies, respectively, and are benchmarked against seven existing techniques across MCAR, MAR, and MNAR scenarios. Results show comparable or superior imputation accuracy to state‑of‑the‑art iterative methods while achieving significant speedups of roughly 5.8× and 9.5× over MissForest.
By Jinwei Li, Michelle Bruch, Daniel Tenbrinck
Probabilistic Regression Trees (PRTrees) are a smooth and consistent alternative to classical regression trees, producing continuous predictions through probabilistic split assignments. This paper extends the PRTree framework to accommodate missing predictor values directly during tree construction, eliminating the need for prior imputation.
arXiv:2608. 06195v1 Announce Type: cross Abstract: Probabilistic Regression Trees (PRTrees) are a smooth and consistent alternative to classical regression trees, producing continuous predictions through probabilistic split assignments.
By Taiane Schaedler Prass, Alisson Silva Neimaier, Guilherme Pumi
The paper introduces SCR-MF, a two‑stage workflow for single‑cell RNA sequencing imputation that first detects dropout events with scRecover and then imputes missing values using the non‑parametric missForest algorithm. Benchmarking on public and simulated datasets shows that SCR‑MF delivers robust, interpretable results that match or surpass existing methods while maintaining biological fidelity. Runtime analysis indicates that SCR‑MF balances accuracy with computational efficiency, making it well suited for mid‑scale single‑cell studies.
By Ali Anaissi, Deshao Liu, Yuanzhe Jia, Weidong Huang, Widad Alyassine, Junaid Akram