Abstract
Given a specific model and a regression function, this work analyses the sensitivity of the kernel estimator on the bandwidth, considered as a function parameter. The problem is well known and has been investigated quite thoroughly. The novelty of our study is that we invert the perspective: instead of examining the estimated regression function and the influence of the bandwidth function, we analyse the complexity of the bandwidth function that is determined by the structure of the process. We show that preliminary evaluation of the structure of the unknown function can improve the results of the kernel regression and, contextually, may significantly simplify the estimation procedure.
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© 2012 Springer-Verlag Italia
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Parrella, M.L. (2012). Evaluating the behavior of a function in kernel based regression. In: Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Milano. https://doi.org/10.1007/978-88-470-2342-0_39
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DOI: https://doi.org/10.1007/978-88-470-2342-0_39
Publisher Name: Springer, Milano
Print ISBN: 978-88-470-2341-3
Online ISBN: 978-88-470-2342-0
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