Year: 2023
Type: preprint
Abstract: Kernel-based modal statistical methods include mode estimation, regression, and clustering. Estimation accuracy of these methods depends on the kernel used as well as the bandwidth. We study effect of the selection of the kernel function to the estimation accuracy of these methods. In particular, we theoretically show a (multivariate) optimal kernel that minimizes its analytically-obtained asymptotic error criterion when using an optimal bandwidth, among a certain kernel class defined via the number of its sign changes.
Source: arXiv (Cornell University)
Authors: Yamasaki, Ryoya, Tanaka, Toshiyuki
Institution: Kyoto University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Award: モードを活用する統計手法の解析及び開発
OpenAlex ID: W4366733145