Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/11448
Title: Discrimination methodology between error processes and bilinear processes
Authors: Gonçalves, E. 
Jacob, P. 
Mendes-Lopes, N. 
Keywords: Time series; Asymptotic separation; Bilinear models; Test
Issue Date: 2002
Publisher: Centro de Matemática da Universidade de Coimbra
Citation: Pré-Publicações DMUC. 02-17 (2002)
Abstract: A new methodology, based on the asymptotic separation of probability laws, was introduced by Gonçalves, Jacob and Mendes-Lopes(2000) in the development of the statistical inference of bilinear models, namely in the construction of a consistent decision procedure for the simple bilinear ones. This paper presents a generalisation of that study by introducing in the procedure a smoother decision statistics. The aim of this decision method is to discriminate between an error process and a simple bilinear model. So, we use it as a consistent test and its consistence is obtained by establishing the asymptotic separation of the sequences of probability laws defined by each hypothesis. The convergence rate of the procedure is studied under the truthfulness of the error process hypothesis. An exponential decay is obtained.
URI: http://hdl.handle.net/10316/11448
Rights: openAccess
Appears in Collections:FCTUC Matemática - Artigos em Revistas Nacionais

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