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Proceedings Paper

General class of chi-square statistics for goodness-of-fit tests for stationary time series
Author(s): Karim Choukri; Eric Moulines
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Paper 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.

Paper Details

Date Published: 28 October 1994
PDF: 12 pages
Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); doi: 10.1117/12.190833
Show Author Affiliations
Karim Choukri, Telecom Paris (France)
Eric Moulines, Telecom Paris (France)


Published in SPIE Proceedings Vol. 2296:
Advanced Signal Processing: Algorithms, Architectures, and Implementations V
Franklin T. Luk, Editor(s)

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