Testing that a stationary time series is Gaussian
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Publication:1102680
DOI10.1214/aos/1176350618zbMath0644.62093OpenAlexW2042381229MaRDI QIDQ1102680
Publication date: 1987
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aos/1176350618
stationary stochastic processesempirical characteristic functiongoodness-of-fit testcomposite hypothesesPearson chi-square testCanadian lynx datatesting GaussianityWolfer sunspot data
Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Non-Markovian processes: hypothesis testing (62M07)
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A random-projection based test of Gaussianity for stationary processes ⋮ DIAGNOSTIC CHECKING FOR THE ADEQUACY OF NONLINEAR TIME SERIES MODELS ⋮ Normality tests for dependent data: large-sample and bootstrap approaches ⋮ Normality testing for a long-memory sequence using the empirical moment generating function ⋮ ON MULTIPLE STRUCTURAL BREAKS IN DISTRIBUTION: AN EMPIRICAL CHARACTERISTIC FUNCTION APPROACH ⋮ Monitoring changes in the error distribution of autoregressive models based on Fourier methods ⋮ Lipschitz-Killing curvatures of excursion sets for two-dimensional random fields ⋮ A SIMPLE TEST OF NORMALITY FOR TIME SERIES ⋮ Time-Varying Periodicity in Intraday Volatility ⋮ On residual empirical processes of stochastic regression models with applications to time series ⋮ Fourier–type tests involving martingale difference processes ⋮ Tests for time series of counts based on the probability-generating function ⋮ Tests for multinormality with applications to time series
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