Please use this identifier to cite or link to this item: http://elea.unisa.it:8080/xmlui/handle/10556/3809
Title: A locally adaptive bandwidth selector for kernel based regression
Authors: Giordano, Francesco
Parrella, Maria Lucia
Keywords: Nonparametric regression
Variable bandwidth selection
Derivative estimation
Neural networks
Local polynomials
Dependent data
Issue Date: 2009
Citation: Giordano, F. and Parrella, M. L. (2009). “A locally adaptive bandwidth selector for kernel based regression”. DISES Working Paper 3.209, Università degli Studi di Salerno, Dipartimento di Scienze Economiche e Statistiche.
Abstract: The selection of the smoothing parameter represents a crucial step in the local polynomial regression, because of the implications on the consistency of the nonparametric regression estimator and because of the difficulties in the implementation of the selection procedure. Moreover, to capture the complexity of the unknown regression curve, a local variable bandwidth is needed, which determines an increase in the efficiency and computa- tional costs of such algorithms. This paper focuses on the problem of the automatic selection of a local bandwidth. We propose a slightly different approach with respect to the traditional ones, which does not require ad- ditional computational effort. The empirical performance of the method is shown in the paper through a simulation study.
URI: http://elea.unisa.it:8080/xmlui/handle/10556/3809
http://dx.doi.org/10.14273/unisa-2031
ISSN: 1971-3029
Appears in Collections:DiSES Working Papers

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