vavra.sample | R Documentation |
Generates a sieve bootstrap sample of the Anderson-Darling statistic test.
vavra.sample(y, normality = c("ad","lobato","jb","cvm","shapiro","epps"),
reps = 1000, h = 100, seed = NULL, c = 1, lambda = c(1,2))
y |
a numeric vector or an object of the |
normality |
A character string naming the desired test for checking normality.
Valid values are |
reps |
an integer with the total bootstrap repetitions. |
h |
an integer with the first |
seed |
An optional |
c |
a positive real value used as argument for the Lobato's test. |
lambda |
a numeric vector used as argument for the Epps's test. |
The Vávra test approximates the empirical distribution function of the Anderson-Darlings statistic, using a sieve bootstrap approximation. The test was proposed by Psaradakis, Z. & Vávra, M (20.17).
This function is the equivalent of xarsieve
of
Psaradakis, Z. & Vávra, M (20.17).
A numeric array with the Anderson Darling sieve bootstrap sample
Asael Alonzo Matamoros.
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.
Psaradakis, Z. & Vávra, M. (2017). A distance test of normality for a wide class of stationary process. Journal of Econometrics and Statistics. 2, 50-60.
Bulmann, P. (1997). Sieve Bootstrap for time series. Bernoulli. 3(2), 123 -148.
epps.statistic
, lobato.statistic
# Generating an stationary arma process
y = arima.sim(100,model = list(ar = 0.3))
adbs = vavra.sample(y)
mean(adbs)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.