mark_boot {boodd} | R Documentation |
Bootstraping Markov chain
Description
The function fo bootstrap the Markov chain using estimator of the transition kernel
Usage
mark_boot(X, func, B, ...)
Arguments
X |
A numeric vector representing a Markov chain. |
func |
The function to apply to each sample. |
B |
A positive integer; the number of bootstrap samples. |
... |
Optional additional arguments for the |
Details
The method is based on estimating the transition kernel of the chain, which is used to generate the bootstrap time series. The transition density is estimated using some Gaussian kernel.
Value
Returns an object of class boodd
.
References
Bertail, P. and Dudek, A. (2025). Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted..
Prakasa Rao, B. L. S. and Kulperger, R. J. (1989). Bootstrapping a finite state Markov chain. Sankhya - Series A, 51, 178-191.
Rajarshi, M.B. (1990). Bootstrap in Markov-Sequences Based on Estimates of Transition Density. Annals of the Institute of Statistical Mathematics, 42, 253-268.
See Also
blockboot
,
regenboot
,
findBestEpsilon
.
Examples
set.seed(12345)
phi=0.6
n=200
X <- arima.sim(list(order=c(1,0,0),ar=phi),n=n)
boo1=mark_boot(X,mean,199)
boot_dist(boo1)
# Compute confidence intervals
confint(boo1,method="all")