nortsTest (version 1.1.2)

cvm_bootstrap.test: The Sieve Bootstrap Cramer Von Mises test for normality.

Description

Performs the approximated Cramer Von Mises test of normality for univariate time series. Computes the p-value using Psaradakis and Vavra's (2020) sieve bootstrap procedure.

Usage

cvm_bootstrap.test(y, reps = 1000, h = 100, seed = NULL)

Value

A list with class "h.test" containing the following components:

statistic:

the sieve bootstrap Cramer Von Mises' statistic.

p.value:

the p value for the test.

alternative:

a character string describing the alternative hypothesis.

method:

a character string “Sieve-Bootstrap Cramer Von Mises' test”.

data.name:

a character string giving the name of the data.

Arguments

y

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

reps

an integer with the total bootstrap repetitions.

h

an integer with the first burn-in sieve bootstrap replicates.

seed

An optional seed to use.

Author

Asael Alonzo Matamoros.

Details

Employs Cramer Von Mises test approximating the p-value using a sieve-bootstrap procedure, Psaradakis, Z. and Vávra, M. (2020).

References

Psaradakis, Z. and Vávra, M. (2020) Normality tests for dependent data: large-sample and bootstrap approaches. Communications in Statistics-Simulation and Computation 49 (2). ISSN 0361-0918.

Bulmann, P. (1997). Sieve Bootstrap for time series. Bernoulli. 3(2), 123 -148.

Stephens, M.A. (1986): Tests based on EDF statistics. In: D'Agostino, R.B. and Stephens, M.A., eds.: Goodness-of-Fit Techniques. Marcel Dekker, New York.

See Also

vavra.test, sieve.bootstrap

Examples

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