Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...

Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...

Some studies of the bootstrap have assessed the effect of smoothing the estimated distribution that is resampled, a process usually known as the smoothed bootstrap. Generally, the smoothed ...

The performance of multivariate kernel density estimates depends crucially on the choice of bandwidth matrix, but progress towards developing good bandwidth matrix selectors has been relatively slow.

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 ...