arXiv stat.ML
· Papers
Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation
arXiv:2607.07767v1 Announce Type: new 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. We recast imputation under missing completely at random (MCAR) as d