bOptEmpProc | R Documentation |

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
function `bOpt()`

is called in the functions
`cpMean()`

, `cpVar()`

, `cpGini()`

,
`cpAutocov()`

, `cpCov()`

,
`cpTau()`

and `detOpenEndCpMean()`

when `b`

is
set to `NULL`

. The function function `bOptEmpProc()`

is
called in the functions `cpDist()`

,
`cpCopula()`

, `cpAutocop()`

,
`stDistAutocop()`

and `simClosedEndCpDist()`

when
`b`

is set to `NULL`

.

bOpt(influ, weights = c("parzen", "bartlett")) bOptEmpProc(x, m=5, weights = c("parzen", "bartlett"), L.method=c("max","median","mean","min"))

`influ` |
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. |

`x` |
a data matrix whose rows are continuous observations. |

`weights` |
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. |

`m` |
a strictly positive integer specifying the number of points of the
uniform grid on |

`L.method` |
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.

`cpDist()`

, `cpCopula()`

,
`cpAutocop()`

, `stDistAutocop()`

,
`cpMean()`

, `cpVar()`

, `cpGini()`

,
`cpAutocov()`

, `cpCov()`

,
`cpTau()`

, `seqOpenEndCpMean`

and
`seqClosedEndCpDist`

.

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