11th International Symposium on Computer Applications in Biotechnology (CAB), Date: 2010/07/01 - 2010/07/01, Location: Leuven, Belgium
Proc. of the 11th International Symposium on Computer Applications in Biotechnology (CAB)
Author:
Keywords:
SISTA
Abstract:
It is a well-known problem that obtaining a correct bandwidth in nonparametric regression is difficult in the presence of correlated errors. There exist a wide variety of methods coping with this problem, but they all critically depend on a tuning procedure which requires accurate information about the correlation structure. Since the errors cannot be observed, the latter is a hard goal to achieve. In this paper, we show the breakdown of several data-driven parameter selection procedures. We also develop a bandwidth selection procedure based on bimodal kernels which successfully removes the error correlation without requiring any prior knowledge about its structure. Some extensions are made to use such a criterion in least squares support vector machines for regression. © 2010 IFAC.