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http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1646
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor | 31249 | es_ES |
dc.contributor.other | https://orcid.org/0000-0002-7337-8974 | - |
dc.coverage.spatial | Global | es_ES |
dc.creator | De la Rosa Vargas, José Ismael | - |
dc.creator | Fleury, Gilles | - |
dc.creator | Davoust, Marie Eve | - |
dc.date.accessioned | 2020-04-14T19:18:24Z | - |
dc.date.available | 2020-04-14T19:18:24Z | - |
dc.date.issued | 2003-08 | - |
dc.identifier | info:eu-repo/semantics/publishedVersion | es_ES |
dc.identifier.issn | 0018-9456 | es_ES |
dc.identifier.issn | 1557-9662 | es_ES |
dc.identifier.uri | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1646 | - |
dc.identifier.uri | https://doi.org/10.48779/7w3h-8v75 | - |
dc.description.abstract | 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. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE Transactions on Instrumentation and Measurement | es_ES |
dc.relation | 10.1109/TIM.2003.814816 | es_ES |
dc.relation.uri | generalPublic | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.source | IEEE Transactios on Instrumentation and Measurement, Vol. 52, No. 4, August 2003, pp. 1009-1020 | es_ES |
dc.subject.classification | INGENIERIA Y TECNOLOGIA [7] | es_ES |
dc.subject.other | Bootstrap | es_ES |
dc.subject.other | indirect measurement | es_ES |
dc.subject.other | Monte Carlo simulation | es_ES |
dc.subject.other | nonlinear regression | es_ES |
dc.subject.other | nonparametric PDF estimation | es_ES |
dc.title | Minimum-Entropy, PDF Approximation, and Kernel Selection for Measurement Estimation | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
Appears in Collections: | *Documentos Académicos*-- M. en Ciencias del Proc. de la Info. |
Files in This Item:
File | Description | Size | Format | |
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1_DelaRosa IEEETIM P1 2003.pdf | DelaRosa IEEETIM 2003 | 757,66 kB | Adobe PDF | View/Open |
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