Selected Applications Related to Kernel Density Estimation
This presents a number of applications related to KDE. The first one is discriminant analysis, a well-known data exploration method. The second is cluster analysis (this is also a well-researched field), where the so-called mean-shift algorithm is used. We illustrate the two areas with some simple numerical examples confirming the practical usability of these KDE-based variants of the algorithms used. Next, the nonparametric kernel regression is presented. It can be viewed as an interesting alternative to the classical parametric regression techniques. The fourth application is multidimensional statistical process control. Here, a kernel-based approach is a worth considering option if the underlying d-variate process is not multivariate normal. The final part is devoted to presenting a complete framework for the so-called gating procedure widely used in analyzing flow cytometry datasets. The framework is based on a smart adaptation of the so-called feature significance technique. To show that it can be used in practical terms, we provide a numerical example based on a real flow cytometry dataset. The described results show that the proposed method can be considered an alternative to classical gating methods.