embb {boodd} | R Documentation |
Characteristics for Extension of Moving Block Bootstrap Class
Description
The class of functions designed for bootstrap samples obtained using the Extension of Moving Block Bootstrap (EMBB) method or its circular version (CEMBB). These functions calculate seasonal means, seasonal variances, and seasonal autocovariances when a periodic time series with period length d is considered. For periodic and almost periodically correlated time series, the functions calculate the Fourier coefficients of the mean and autocovariance functions.
Usage
## S3 method for class 'embb'
seasonalMean(x, period, ...)
## S3 method for class 'embb'
seasonalVar(x, period, ...)
## S3 method for class 'embb'
seasonalACF(x, tau, period, ...)
## S3 method for class 'embb'
meanCoeff(x, period, freq, ...)
## S3 method for class 'embb'
acfCoeff(x, tau, period, freq, ...)
Arguments
x |
An object of class |
period |
An integer; period length of the original data. |
tau |
An integer or vector of integers; single lag or vector of lags. |
freq |
A vector of real numbers; vector of frequencies. |
... |
Additional arguments. |
Details
These methods apply to objects of class embb
typically obtained using the embb.sample
function.
Value
-
seasonalMean
andseasonalVar
return a vector of lengthperiod
. -
seasonalACF
returns either:A vector of length
period
if a single lagtau
is specified.A matrix with
length(tau)
rows andperiod
columns iftau
is a vector.
-
meanCoeff
returns a vector of the same length asfreq
. -
acfCoeff
returns either:A vector of length
length(freq)
if a single lagtau
is specified.A matrix with
length(tau)
rows andlength(freq)
columns iftau
is a vector.
References
Bertail, P., & Dudek, A. (2025). Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted.
Dudek, A. E. (2015). Circular block bootstrap for coefficients of autocovariance function of almost periodically correlated time series. Metrika, 78, 313-335.
Dudek, A. E. (2018). Block bootstrap for periodic characteristics of periodically correlated time series. Journal of Nonparametric Statistics, 30, 87-124.
See Also
embb.sample
, seasonalMean.default
, seasonalVar.default
, seasonalACF.default
Examples
# Generate a periodically correlated time series
set.seed(123)
n=200
b <- arima.sim(n = n, model = list(ar = c(0.5, 0.4), na = 0.5))
period <- 12
x <- 5 * cos(2 * pi / period * (1:n)) + 5 * b * cos(2 * pi / period * (1:n))
X_ts <- ts(x)
bootstrapped_X <- embb.sample(X_ts, length.block = 15, method = "movingblock")
acf_results <- acfCoeff(bootstrapped_X, tau = 0, freq = 0)
mean_seasonal <- seasonalMean(bootstrapped_X, period = period)