Kernel density estimation and its application

作者：Stanisław Węglarczyk · 发表于：ITM Web of Conferences · 年份：2018 · DOI：10.1051/itmconf/20182300037 · 被引用次数：656 · 研究领域：Statistical Methods and Inference、Advanced Statistical Methods and Models、Neural Networks and Applications

Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel technique produces smooth estimate of the pdf, uses all sample points' locations and more convincingly suggest multimodality. In its two-dimensional applications, kernel estimation is even better as the 2D histogram requires additionally to define the orientation of 2D bins. Two concepts play fundamental role in kernel estimation: kernel function shape and coefficient of smoothness, of which the latter is crucial to the method. Several real-life examples, both for univariate and bivariate applications, are shown.