Bandwidth matrix selectors for kernel regression
Jan Koláček () and
Ivana Horová ()
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Jan Koláček: Masaryk University
Ivana Horová: Masaryk University
Computational Statistics, 2017, vol. 32, issue 3, 1027-1046
Abstract Choosing a bandwidth matrix belongs to the class of significant problems in multivariate kernel regression. The problem consists of the fact that a theoretical optimal bandwidth matrix depends on the unknown regression function which to be estimated. Thus data-driven methods should be applied. A method proposed here is based on a relation between asymptotic integrated square bias and asymptotic integrated variance. Statistical properties of this method are also treated. The last two sections are devoted to simulations and an application to real data.
Keywords: Multivariate kernel regression; Constrained bandwidth matrix; Kernel smoothing; Mean integrated square error (search for similar items in EconPapers)
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