f_PseudoBlocks {boodd} | R Documentation |
Compute the Value of the Function on a (Pseudo)-Regenerative Blocks.
Description
Function is an adaptation of GetPseudoBlocks
to compute the value of any function on pseudo-regenerative blocks.
Usage
f_PseudoBlocks(x, s, eps_opt, delta_opt, p_XiXip1, func = sum)
Arguments
x |
A vector or time series. |
s |
A real number specifying the center of the small set. |
eps_opt |
A numeric value for the size of the small set. |
delta_opt |
A numeric value for the lower bound in the minorization condition. |
p_XiXip1 |
A numeric value representing the estimator of the transition density. |
func |
A function to apply to each block. Default is |
Details
This function computes the value of a specified function on pseudo-regenerative blocks
of a time series. It uses parameters such as the central value (s
),
the size of the small set (eps_opt
),
and the lower bound in the minorization condition (delta_opt
).
Robustification is not proposed here
due to the complexity of the pseudo-regenerative procedure.
Value
A matrix with two columns:
-
Subf
- the value of the functionfunc
on each sub-block, -
lB
- the length of the sub-blocks on which the function is computed.
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 Clémençon, S. (2006). Regenerative block bootstrap for Markov chains. Bernoulli, 12, 689-712.
See Also
GetPseudoBlocks
, regenboot
, findBestEpsilon
,
GetBlocks
, smallEnsemble
.
Examples
n=1000
coeff=0.75
X = arima.sim(n=n, list(ar = c(coeff)))
sm <- findBestEpsilon(X,s=0,plotIt=FALSE)
eps = sm$epsilon
delta = sm$delta
m = sm$s
f = sm$trans
result <- f_PseudoBlocks(X, 0, eps, delta, f, func = max)
print(result)