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 ...

This is a preview. Log in through your library . Abstract A kernel density estimator is defined to be admissible if no other kernel estimator has (among all densities ...

In this paper we show how one canimplement in practice the bandwidth selection in deconvolution recursive kernel estimators of a probability density function defined by the stochastic approximation ...

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 ...

Refer to Silverman (1986) or Scott (1992) for an introduction to nonparametric density estimation. PROC MODECLUS uses (hyper)spherical uniform kernels of fixed or variable radius. The density estimate ...