Rboot: Risk estimation for a tapered covariance matrix estimator via...

In slm: Stationary Linear Models

Description

This function computes an estimation of the risk for the tapered covariance matrix estimator of a process via a bootstrap method, for a specified treshold and a specified kernel.

Usage

Rboot(epsilon, treshold, block_size, block_n, model_max, kernel_fonc)

Arguments

epsilon	numeric vector. An univariate process.
treshold	integer. Number of estimated autocovariance terms that we consider for the estimation of the covariance matrix.
block_size	integer. The size of the bootstrap blocks. block_size must be greater than model_max.
block_n	integer. Blocks number used for the bootstrap.
model_max	integer. The maximal dimension, that is the maximal number of terms available to estimate the covariance matrix.
kernel_fonc	function. The kernel to use. The user can define his own kernel and put it in the argument.

Value

This function returns a list with:

risk	for one treshold, the value of the estimated risk.
SE	the standard-error due to the bootstrap.

References

E. Caron, J. Dedecker and B. Michel (2019). Linear regression with stationary errors: the R package slm. arXiv preprint arXiv:1906.06583. https://arxiv.org/abs/1906.06583.

slm documentation built on Aug. 31, 2020, 5:11 p.m.