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A Review and Comparison of Bandwidth Selection Methods for Kernel Regression
Author(s) -
Köhler Max,
Schindler Anja,
Sperlich Stefan
Publication year - 2014
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12039
Subject(s) - bandwidth (computing) , computer science , kernel regression , selection (genetic algorithm) , regression , statistics , kernel (algebra) , kernel method , machine learning , artificial intelligence , data mining , mathematics , support vector machine , telecommunications , combinatorics
Summary Over the last decades, several methods for selecting the bandwidth have been introduced in kernel regression. They differ quite a bit, and although there already exist more selection methods than for any other regression smoother, one can still observe coming up new ones. Given the need of automatic data‐driven bandwidth selectors for applied statistics, this review is intended to explain and, above all, compare these methods. About 20 different selection methods have been revised, implemented and compared in an extensive simulation study.