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 func function.

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")

[Package boodd version 0.1 Index]