Kernel density estimation (KDE) is a cornerstone of non-parametric statistics, offering a flexible means to infer an underlying probability density from finite samples without assuming a predetermined ...

Adaptive density estimation techniques in nonparametric statistics aim to reconstruct an unknown probability density from sample data without imposing rigid parametric forms, while automatically ...

Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular ...