WN_test | R Documentation |
WN_test()
is the test proposed in Chang, Yao and Zhou
(2017) for the following hypothesis testing problems:
H_0:\{{\bf x}_t \}_{t=1}^n\mathrm{\ is\ white\ noise\ \ versus\ \ }H_1:\{{\bf x}_t \}_{t=1}^n\mathrm{\ is\ not\ white\ noise.}
WN_test( X, lag.k = 2, B = 2000, kernel.type = c("QS", "Par", "Bart"), pre = FALSE, alpha = 0.05, k0 = 5, thresh = FALSE, tuning.vec = NULL )
X |
{\bf X} = \{{\bf x}_1, … , {\bf x}_n \}', an n\times p sample matrix, where n is the sample size and p is the dimension of {\bf x}_t. |
lag.k |
Time lag K, a positive integer, used to calculate the test
statistic [See (4) in Chang, Yao and Zhou (2017)]. Default is |
B |
Bootstrap times for generating multivariate normal distributed
random vectors in calculating the critical value. Default is |
kernel.type |
String, an option for choosing the symmetric kernel used
in the estimation of long-run covariance matrix, for example, |
pre |
Logical value which determines whether to performs preprocessing
procedure on data matrix |
alpha |
The prescribed significance level. Default is 0.05. |
k0 |
A positive integer specified to calculate \widehat{{\bf
W}}_y. See parameter |
thresh |
Logical. It determines whether to perform the threshold method
to estimate \widehat{{\bf W}}_y or not. See parameter |
tuning.vec |
The value of thresholding tuning parameter λ.
See parameter |
An object of class "WN_test" is a list containing the following components:
reject |
Logical value. If |
p.value |
Numerical value which represents the p-value of the test based on the observed data \{{\bf x}_t\}_{t=1}^n. |
Chang, J., Yao, Q. & Zhou, W. (2017). Testing for high-dimensional white noise using maximum cross-correlations, Biometrika, Vol. 104, pp. 111–127.
Chang, J., Guo, B. & Yao, Q. (2018). Principal component analysis for second-order stationary vector time series, The Annals of Statistics, Vol. 46, pp. 2094–2124.
Cai, T. and Liu, W. (2011). Adaptive thresholding for sparse covariance matrix estimation, Journal of the American Statistical Association, Vol. 106, pp. 672–684.
PCA4_TS
n <- 200 p <- 10 X <- matrix(rnorm(n*p),n,p) res <- WN_test(X) Pvalue <- res$p.value rej <- res$reject
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