Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng/handle/123456789/5337
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dc.contributor.authorUdomboso, C. G.-
dc.contributor.authorAmahia, G. N.-
dc.contributor.authorDontwi, I. K.-
dc.date.accessioned2021-05-25T10:53:41Z-
dc.date.available2021-05-25T10:53:41Z-
dc.date.issued2016-
dc.identifier.issn1538-9472-
dc.identifier.otherui_art_udomboso_adjusted_2016-
dc.identifier.otherJournal of Modern Applied Statistical Methods 15(2), pp. 411-427-
dc.identifier.urihttp://ir.library.ui.edu.ng/handle/123456789/5337-
dc.description.abstractIn this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC) criterion, based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The ANIC improves model selection in more sample sizes than does the NIC.en_US
dc.language.isoenen_US
dc.publisherJMASM, Inc.en_US
dc.subjectStatistical neural networken_US
dc.subjectNetwork information criterionen_US
dc.subjectNetwork information criterionen_US
dc.subjectAdjusted network information criterionen_US
dc.subjectTransfer functionen_US
dc.titleAn adjusted network information criterion for model selection in statistical neural network modelsen_US
dc.typeArticleen_US
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