func_fdb {boodd} | R Documentation |
Functional Bootstrap in the Frequency Domain (FDB)
Description
Uses the Frequency Domain Bootstrap (FDB) to compute the bootstrap spectral density, cumulative distribution function for the estimated spectral density, and the quantiles of the standardized distribution.
Usage
func_fdb(
x,
B,
kernel = "normal",
bandwidth,
p = 0.5,
PLT = c("spec", "cdf", NULL)
)
Arguments
x |
A vector or time series. |
B |
A positive integer; the number of bootstrap replications. |
kernel |
A character string specifying the smoothing kernel. The valid values are:
|
bandwidth |
A real number; the kernel bandwidth smoothing parameter.
If unspecified, an optimal value is computed using formula |
p |
A vector of the quantiles to be calculated. Default is |
PLT |
An argument specifying what to plot:
|
Details
The function performs a bootstrap in the frequency domain and computes the specified functionals. It estimates the spectral density using a periodogram smoothed by the specified kernel and bandwidth. The function then computes the cumulative spectral density and quantiles based on this estimation.
Value
The function returns a list of two components:
The boodd object:
-
s
: A matrix of sizeB \times (2n_0 + l_p)
, wheren_0 = \lfloor n/2 \rfloor
andl_p
is the length of vectorp
. The firstn_0
columns contain bootstrap values of the spectral density at the frequency2\pi n_0
. The columnsn_0+1
up to2n_0
contain values of the cumulative distribution function corresponding to the bootstrap spectral density, and the lastl_p
columns contain the estimated quantiles. -
Tn
: Estimated values of the spectral density, the estimated cumulative distribution of the spectral density, and the quantiles.
-
-
freqs
: The vector of frequencies at which the functionals are computed for the processx
.
References
Bertail, P. and Dudek, A. (2025). Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted.
Bertail, P. and Dudek, A.E. (2021). Consistency of the Frequency Domain Bootstrap for differentiable functionals, Electron. J. Statist., 15, 1-36.
Hurvich, C. M. and Zeger, S. L. (1987). Frequency domain bootstrap methods for time series, Technical Report 87-115, Graduate School of Business Administration, New York Univ.
Lahiri, S.N. (2003). Resampling Methods for Dependent Data. Springer, New York.
See Also
aidedboot
,tft_boot
, aidedboot
,
freqboot
, per_boo
.
Examples
# Choice of sample size
n <- 1000
# Simulate AR(1) model with parameter 0.6
x <- arima.sim(list(order=c(1,0,0),ar=0.6),n=n)
n0=floor(n/2)
bf=func_fdb(x,199, PLT="spec")