perfectSample

Samples an MTD Markov Chain from the stationary distribution.

Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, 'hdMTD' includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) <http://jmlr.org/papers/v24/22-0266.html>.

Maiara Gripp

hdMTD

Inference for High-Dimensional Mixture Transition Distribution
Models

Guilherme Ost

Giulio Iacobelli

perfectSample function

<dl><dt>MTD</dt>
<dd>An MTD object, see <code>MTDmodel()</code> for properly generating a MTD object.</dd>
<dt>N</dt>
<dd>The sample size. If <code>NULL</code> sample size will be set to 1000.</dd></dl>

Arguments

Perfectly samples an MTD Markov chain — perfectSample

<dl>

<dt>MTD</dt>
<dd>An MTD object, see <code>MTDmodel()</code> for properly generating a MTD object.</dd>


<dt>N</dt>
<dd>The sample size. If <code>NULL</code> sample size will be set to 1000.</dd>

</dl>

perfectSample: Perfectly samples an MTD Markov chain

Description

Usage

Value

Arguments

Details

Examples