rmnorm

This function generates multivariate normal errors that are used in the
generation of the Bifurcating autoregressive tree.

Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020) <doi:10.1002/sta4.342>. Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023)
<doi:10.6339/23-JDS1092>.

Tamer Elbayoumi

bifurcatingr

Bifurcating Autoregressive Models

Sayed Mostafa

rmnorm function

<dl><dt>n</dt>
<dd>sample size</dd>
<dt>d</dt>
<dd>dimension. Defaults to 2 for bivariate normal errors.</dd>
<dt>mu</dt>
<dd>mean vector. Defaults to the zero vector.</dd>
<dt>sigma</dt>
<dd>variance-covariance matrix. Defaults to the <code>d</code> by <code>d</code>
identity matrix, where d is the dimension.</dd></dl>

Arguments

Multivariate Normal Generator — rmnorm

<dl>

<dt>n</dt>
<dd>sample size</dd>


<dt>d</dt>
<dd>dimension. Defaults to 2 for bivariate normal errors.</dd>


<dt>mu</dt>
<dd>mean vector. Defaults to the zero vector.</dd>


<dt>sigma</dt>
<dd>variance-covariance matrix. Defaults to the <code>d</code> by <code>d</code>
identity matrix, where d is the dimension.</dd>

</dl>

rmnorm: Multivariate Normal Generator

Description

Usage

Value

Arguments

Examples