General class of chi-square statistics for goodness-of-fit tests for stationary time series

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Abstract

In this contribution, a class of time-domain goodness-of-fit procedures for stationary time- series, is presented. These test procedures are based on minimum chi-square statistics in the deviations of certain sample statistics (obtained from finite-memory non-linear transformations of the process) from their ensemble counterparts. Two specific versions are derived, depending on the parameterization of the model manifold. Exact asymptotic distribution of these tests under the null hypothesis HO and local alternatives are derived. Two applications of this general procedure is finally presented, aiming at assessing that (1) a stationary scalar time-series is autoregressive and (2) that a multivariate stationary time-series is a noisy instantaneous mixture of independent scalar time-series.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsFranklin T. Luk
PublisherSociety of Photo-Optical Instrumentation Engineers
Pages201-212
Number of pages12
ISBN (Print)0819416207
Publication statusPublished - 1 Dec 1994
EventAdvanced Signal Processing: Algorithms, Architectures, and Implementations V - San Diego, CA, USA
Duration: 24 Jul 199427 Jul 1994

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume2296
ISSN (Print)0277-786X

Conference

ConferenceAdvanced Signal Processing: Algorithms, Architectures, and Implementations V
CitySan Diego, CA, USA
Period24/07/9427/07/94

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