Document worth reading: “A Tutorial on Kernel Density Estimation and Recent Advances”

This tutorial offers a gentle introduction to kernel density estimation (KDE) and newest advances regarding confidence bands and geometric/topological choices. We begin with a dialogue of basic properties of KDE: the convergence cost under quite a few metrics, density spinoff estimation, and bandwidth selection. Then, we introduce widespread approaches to the event of confidence intervals/bands, and we focus on the correct method to cope with bias. Next, we talk about newest advances inside the inference of geometric and topological choices of a density carry out using KDE. Finally, we illustrate how one can use KDE to estimate a cumulative distribution carry out and a receiver working attribute curve. We current R implementations related to this tutorial on the end. A Tutorial on Kernel Density Estimation and Recent Advances