random.projection: Generate a random projection

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/rp_test.R

Description

Generates a random projection of a univariate stationary stochastic process. Using a beta(shape1,shape2) distribution.

Usage

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random.projection(y,shape1,shape2,seed = NULL)

Arguments

y

a numeric vector or an object of the ts class containing a stationary time series.

shape1

an optional real value with the first shape parameters of the beta distribution.

shape2

an optional real value with the second shape parameters of the beta distribution.

seed

An optional seed to use.

Details

Generates one random projection of a stochastic process using a beta distribution. For more details, see: Nieto-Reyes, A.,Cuesta-Albertos, J. & Gamboa, F. (2014).

Value

a real vector with the projected stochastic process.

Author(s)

Alicia Nieto-Reyes and Asael Alonzo Matamoros

References

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.Result

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.

See Also

lobato.test epps.test

Examples

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# Generating an stationary arma process
y = arima.sim(100,model = list(ar = 0.3))
rp.test(y)

nortsTest documentation built on June 17, 2021, 5:06 p.m.