Subsetting and identification of optimal models in one-dimensional bilinear time series modeling / Dahud Kehinde Shangodoyin in International journal of management science and engineering management, Vol. 5 N° 4 (Août 2010)  

Public ISBD [article]  inInternational journal of management science and engineering management > Vol. 5 N° 4 (Août 2010) . - pp.252-260 Titre : Subsetting and identification of optimal models in one-dimensional bilinear time series modeling Type de document : texte imprimé Auteurs : Dahud Kehinde Shangodoyin, Auteur ; Johnson Funminiyi Ojo, Auteur ; Marcin Kozak, Auteur Année de publication : 2011 Article en page(s) : pp.252-260 Note générale : Management Langues : Anglais (eng) Mots-clés : Non-linear least square Newton-raphson Residual variance Akaike information criterion Stationarity. Résumé : To date, significant efforts have been made to study the theory of bilinear time series models, especially simple bilinear models. Much less efforts, however, have been made to identify optimal models. Focused on optimal model identification, this study attempts to fill this gap. Full and subset one-dimensional bilinear models are proposed and shown to be robust in achieving stationarity for all non-linear series. The parameters of the models are estimated using robust nonlinear least-square method and Newton-Raphson iterative method, and statistical properties of the derived estimates are investigated. An algorithm is proposed to eliminate redundant parameters from full order bilinear models. DEWEY : 658 ISSN : 1750-9653 En ligne : http://www.ijmsem.org/OnlineJournal.do/?141.html [article] Subsetting and identification of optimal models in one-dimensional bilinear time series modeling [texte imprimé] / Dahud Kehinde Shangodoyin, Auteur ; Johnson Funminiyi Ojo, Auteur ; Marcin Kozak, Auteur . - 2011 . - pp.252-260. Management Langues : Anglais (eng) in International journal of management science and engineering management > Vol. 5 N° 4 (Août 2010) . - pp.252-260 Mots-clés : Non-linear least square Newton-raphson Residual variance Akaike information criterion Stationarity. Résumé : To date, significant efforts have been made to study the theory of bilinear time series models, especially simple bilinear models. Much less efforts, however, have been made to identify optimal models. Focused on optimal model identification, this study attempts to fill this gap. Full and subset one-dimensional bilinear models are proposed and shown to be robust in achieving stationarity for all non-linear series. The parameters of the models are estimated using robust nonlinear least-square method and Newton-Raphson iterative method, and statistical properties of the derived estimates are investigated. An algorithm is proposed to eliminate redundant parameters from full order bilinear models. DEWEY : 658 ISSN : 1750-9653 En ligne : http://www.ijmsem.org/OnlineJournal.do/?141.html

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