This paper characterizes the bandwidth value (h) that is optimal for estimating parameters of the form $\eta = E[\omega /f_{V|\mathbb{U}} (V|\mathbb{U})]$ , where the conditional density of a scalar ...

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 ...

This is a preview. Log in through your library . Abstract We establish the uniform almost-sure convergence of a kernel estimate of the conditional density for an ergodic process. A useful application ...

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 ...