Bandwidth selection for kernel density estimation: a Hermite series-based direct plug-in approach (Journal Article)
| <Specific Information> <Reference List> | |
| Type: | Journal Article |
| National /International: | International |
| Title: | Bandwidth selection for kernel density estimation: a Hermite series-based direct plug-in approach |
| Publication Date: | 2020 |
| Authors: |
- Carlos Tenreiro
|
| Journal Name: | Journal of Statistical Computation and Simulation |
| Volume: | 90 |
| Number: | 18 |
| Pages: | 3433-3453 |
| Abstract: | In this paper we propose a new class of Hermite series-based direct plug-in bandwidth selectors for kernel density estimation and we describe their asymptotic and finite sample behaviours. Unlike the direct plug-in bandwidth selectors considered in the literature, the proposed methodology does not involve multistage strategies and reference distributions are no longer needed. The new bandwidth selectors show a good finite sample performance when the underlying probability density function presents not only "easy-to-estimate" but also "hard-to-estimate" distribution features. This quality, that is not shared by other widely used bandwidth selectors as the classical plug-in or the least-square cross-validation methods, is the most significant aspect of the Hermite series-based direct plug-in approach to bandwidth selection. |
| Download: | Not available |
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