Class of time-domain procedures for testing that a stationary time series is Gaussian

Eric Moulines; Karim Choukri; Jean-Francois Cardoso

doi:10.1117/12.190829

28 October 1994 Class of time-domain procedures for testing that a stationary time series is Gaussian

Eric Moulines, Karim Choukri, Jean-Francois Cardoso

Proceedings Volume 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V; (1994) https://doi.org/10.1117/12.190829
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

In this contribution, a class of time-domain procedures for testing that a stationary time-series is Gaussian, is presented. These tests are based on minimum chi-square statistics in the deviations of certain sample statistics from their ensemble counterpart. Exact asymptotic distributions of these tests are derived under the null hypothesis of Gaussianity and under a class of local and fixed alternatives. Two specific tests are then developed, based respectively on the third-order and the fourth-order moments and on the characteristic functions. Extensive simulations are presented to illustrate the power of the test against various alternatives (including additive and non-additive contaminations and non-linear serial dependence.

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Eric Moulines, Karim Choukri, and Jean-Francois Cardoso "Class of time-domain procedures for testing that a stationary time series is Gaussian", Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); https://doi.org/10.1117/12.190829

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KEYWORDS

Statistical analysis

Contamination

Autoregressive models

Statistical modeling

Error analysis

Data modeling

Inspection

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