In the context of the standard CUSUM test based on the sample mean or
in a particular empirical process setting, the following functions
estimate the bandwidth parameter controlling the serial dependence
when generating dependent multiplier sequences using the 'moving
average approach'; see Section 5 of the third reference. The function
bOpt() is called in the functions
NULL. The function function
called in the functions
b is set to
bOpt(influ, weights = c("parzen", "bartlett")) bOptEmpProc(x, m=5, weights = c("parzen", "bartlett"), L.method=c("max","median","mean","min"))
a numeric containing the relevant influence coefficients, which, in the case of the standard CUSUM test based on the sample mean, are simply the available observations; see also the last reference.
a data matrix whose rows are continuous observations.
a string specifying the kernel for creating the weights used in the generation of dependent multiplier sequences within the 'moving average approach'; see Section 5 of the third reference.
a strictly positive integer specifying the number of points of the
uniform grid on (0,1)^d (where d is
a string specifying how the parameter
The implemented approach results from an adaptation of the procedure described in the first two references (see also the references therein). The use of theses functions in a context different from that considered in the third or fourth reference may not be meaningful.
Acknowledgment: Part of the code of the function results from an adaptation of R code of C. Parmeter and J. Racine, itself an adaptation of Matlab code by A. Patton.
A strictly positive integer.
D.N. Politis and H. White (2004), Automatic block-length selection for the dependent bootstrap, Econometric Reviews 23(1), pages 53-70.
D.N. Politis, H. White and A.J. Patton (2004), Correction: Automatic block-length selection for the dependent bootstrap, Econometric Reviews 28(4), pages 372-375.
A. Bücher and I. Kojadinovic (2016), A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing, Bernoulli 22:2, pages 927-968, https://arxiv.org/abs/1306.3930.
A. Bücher and I. Kojadinovic (2016), Dependent multiplier bootstraps for non-degenerate U-statistics under mixing conditions with applications, Journal of Statistical Planning and Inference 170 pages 83-105, https://arxiv.org/abs/1412.5875.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.