A Brief Survey of Bandwidth Selection for Density Estimation 论文
1996Journal of the American Statistical Association引用 302
Advanced Data Compression TechniquesSparse and Compressive Sensing TechniquesBayesian Methods and Mixture Models
摘要
Abstract There has been major progress in recent years in data-based bandwidth selection for kernel density estimation. Some “second generation” methods, including plug-in and smoothed bootstrap techniques, have been developed that are far superior to well-known “first generation” methods, such as rules of thumb, least squares cross-validation, and biased cross-validation. We recommend a “solve-the-equation” plug-in bandwidth selector as being most reliable in terms of overall performance. This article is intended to provide easy accessibility to the main ideas for nonexperts.