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Goodness-of-fit tests for correlated data

GASSER, THEO

In: Biometrika, 1975, vol. 62, no. 3, p. 563-570

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    Titre
    • Goodness-of-fit tests for correlated data
    Auteur
    • GASSER, THEO. Fachgruppe für Statistik, Eidgenössische Technische Hochschule, Zürich
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Biometrika, 1975, vol. 62, no. 3, p. 563-570. Oxford University Press
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:290281
    Summary
    SUMMARY Goodness-of-fit tests for stationary processes are a problem of practical importance, e.g. in the analysis of electroencephalographic data. The distribution of the chi-squared statistic under the normal hypothesis is studied by simulation; power is investigated by an inverse filtering procedure for processes which can be well represented by an autoregressive-moving average model. For a second model, consisting of a Gaussian or non-Gaussian signal plus Gaussian noise, sample skewness and kurtosis are suggested as test statistics. The asymptotic normality and the asymptotic variance of these statistics are derived, as well as the behaviour for a broad class of alternatives. The second model is of primary interest in E.E.G.-analysis