Perform a normality test. The null hypothesis (H0) is that the given data follows a stationary Gaussian process.
a numeric vector or an object of the
A character string naming the desired test for checking normality. Valid values are
Level of the test, possible values range from 0.01 to 0.1. By default
Several different tests are available:
"rp" test are for testing normality
in stationary process.
"shapiro" tests are for numeric data.
In all cases, the alternative hypothesis is that
y follows a Gaussian process. By default,
alpha = 0.05 is used to select the more likely hypothesis.
An h.test class with the main results of normal hypothesis test.
Asael Alonzo Matamoros
Epps, T.W. (1987). Testing that a stationary time series is Gaussian. The Annals of Statistic. 15(4), 1683-1698.
Lobato, I., & Velasco, C. (2004). A simple test of normality in time series. Journal of econometric theory. 20(4), 671-689.
Psaradakis, Z. & Vavra, M. (2017). A distance test of normality for a wide class of stationary process. Journal of Econometrics and Statistics. 2, 50-60.
Nieto-Reyes, A., Cuesta-Albertos, J. & Gamboa, F. (2014). A random-projection based test of Gaussianity for stationary processes. Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 124-141.
Patrick, R. (1982). An extension of Shapiro and Wilk's W test for normality to large samples. Journal of Applied Statistics. 31, 115-124.
Cromwell, J. B., Labys, W. C. & Terraza, M. (1994). Univariate Tests for Time Series Models. Sage, Thousand Oaks, CA. 20-22.
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