Artículos de revistas
Minimum-Entropy, PDF Approximation, and Kernel Selection for Measurement Estimation
Fecha
2003-08Registro en:
0018-9456
1557-9662
Autor
De la Rosa Vargas, José Ismael
Fleury, Gilles
Davoust, Marie Eve
Institución
Resumen
The purpose of this paper is to investigate the selection of an appropriate kernel to be used in a recent robust approach called minimum-entropy estimator (MEE). This MEE estimator is extended to measurement estimation and pdf approximation when p(e) is unknown. The entropy criterion is constructed on the basis of a symmetrized kernel estimate p_hat (e) of p(e). The MEE performance is generally better than the Maximum Likelihood (ML) estimator. The bandwidth selection procedure is a crucial task to assure consistency of kernel estimates. Moreover, recent proposed Hilbert kernels avoid the use of bandwidth, improving the consistency of the kernel estimate. A comparison between results obtained with normal, cosine and Hilbert kernels is presented.