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BANDWIDTH SELECTION IN KERNEL SMOOTHING OF TIME SERIES
Author(s) -
Kim Tae Yoon,
Cox Dennis D.
Publication year - 1996
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1996.tb00264.x
Subject(s) - smoothing , kernel smoother , mathematics , kernel (algebra) , series (stratigraphy) , kernel regression , selection (genetic algorithm) , bandwidth (computing) , kernel method , mathematical optimization , algorithm , statistics , regression , computer science , machine learning , radial basis function kernel , discrete mathematics , support vector machine , paleontology , biology , computer network
. The kernel smoothing method has been considered as a useful tool for identification and prediction in time series models. In practice this method is to be tuned by a smoothing parameter. For selection of the smoothing parameter, Härdle and Vieu (Kernel regression smoothing of time series. J. Time Ser. Anal. 13(1992), 209–32) considered a cross‐validation rule and proved its asymptotic optimality. In this paper we strengthen their result for a wider use of the kernel smoothing of time series.