dc.date.accessioned | 2019-11-08T13:51:51Z | |
dc.date.available | 2019-11-08T13:51:51Z | |
dc.description.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. | it_IT |
dc.language.iso | en | it_IT |
dc.relation.ispartof | Working Papers ; 3.209 | it_IT |
dc.identifier.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. | it_IT |
dc.title | A locally adaptive bandwidth selector for kernel based regression | it_IT |
dc.source | UniSa. Sistema Bibliotecario di Ateneo | it_IT |
dc.contributor.author | Giordano, Francesco | |
dc.contributor.author | Parrella, Maria Lucia | |
dc.date.issued | 2009 | |
dc.identifier.uri | http://elea.unisa.it:8080/xmlui/handle/10556/3809 | |
dc.identifier.uri | http://dx.doi.org/10.14273/unisa-2031 | |
dc.type | Working Paper | it_IT |
dc.format.extent | 38 p. | it_IT |
dc.identifier.issn | 1971-3029 | it_IT |
dc.subject | Nonparametric regression | it_IT |
dc.subject | Variable bandwidth selection | it_IT |
dc.subject | Derivative estimation | it_IT |
dc.subject | Neural networks | it_IT |
dc.subject | Local polynomials | it_IT |
dc.subject | Dependent data | it_IT |