Bandwidth parameter estimation

Description

In a particular empirical process setting, estimates the bandwidth parameter controlling the serial dependence when generating dependent multiplier sequences using the 'moving average approach'; see Section 5 of the third reference. This function is called in the functions cpTestFn() and cpTestCn() if b is set to NULL.

Usage

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

Arguments

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 (0,1)^d (where d is ncol(x)) involved in the estimation of the bandwidth parameter; see Section 5 of the third reference. The number of points of the grid is given by m^ncol(x) so that m needs to be decreased as d increases.

L.method

a string specifying how the parameter L involved in the estimation of the bandwidth parameter is computed; see Section 5 of the third reference.

Details

The implemented approach results from an adaptation of the procedure described in the first two references (see also the references therein). The use of this function in a context different from that considered in the third 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.

Value

A strictly positive integer.

References

D.N. Politis and H. White (2004), Automatic block-length selection for the dependent bootstrap, Econometric Reviews 23(1):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):372-375.

A. Bücher and I. Kojadinovic (2014), A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing, Bernoulli, in press, http://arxiv.org/abs/1306.3930.

See Also

cpTestFn(), cpTestCn().