Kernel density classification for spherical data

Autor:	Agnese Panzera, Charles C. Taylor, Marco Di Marzio, Stefania Fensore
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	Statistics and Probability 010104 statistics & probability Field (physics) Global climate 010102 general mathematics Kernel density estimation Nonparametric statistics Applied mathematics Decision rule 0101 mathematics Statistics Probability and Uncertainty 01 natural sciences Mathematics
ISSN:	0167-7152
Popis:	Classifying observations coming from two different spherical populations by using a nonparametric method appears to be an unexplored field, although clearly worth to pursue. We propose some decision rules based on spherical kernel density estimation and we provide asymptotic L 2 properties. A real-data application using global climate data is finally discussed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d97cf444bc4f7639d6f258f7d69179a https://eprints.whiterose.ac.uk/138925/1/sphereclassA5.pdf Zobrazit plný text záznamu Full Text from ScienceDirect