Minimum-Entropy, PDF Approximation, and Kernel Selection for Measurement Estimation
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...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2003 |
| País: | México |
| Institución: | Universidad Autónoma de Zacatecas |
| Repositorio: | Repositorio Institucional Caxcán |
| Idioma: | inglés |
| OAI Identifier: | oai:http://ricaxcan.uaz.edu.mx:20.500.11845/1646 |
| Acceso en línea: | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1646 https://doi.org/10.48779/7w3h-8v75 |
| Access Level: | acceso abierto |
| Palabra clave: | INGENIERIA Y TECNOLOGIA [7] Bootstrap indirect measurement Monte Carlo simulation nonlinear regression nonparametric PDF estimation |
| Sumario: | 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. |
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