em_rinit

etk2tau

Gaussian EM-step with random initialisation.

 EM 

Model time series using mixture autoregressive (MAR)
models. Implemented are frequentist (EM) and Bayesian
methods for estimation, prediction and model
evaluation. See Wong and Li (2002)
<doi:10.1111/1467-9868.00222>, Boshnakov (2009)
<doi:10.1016/j.spl.2009.04.009>), and the extensive
references in the documentation.

Georgi Boshnakov

mixAR

Mixture Autoregressive Models

Davide Ravagli

em_rinit function

<dl> <dt>y</dt>
<dd>time series.</dd> <dt>order</dt>
<dd>MixAR order, vector of length the number of components.</dd> <dt>partempl</dt>
<dd>parameter template, a list containing one element for each mixture
 component, see <code>randomArCoefficients</code>.</dd> <dt>etk</dt>
<dd>MixAR component residuals, a matrix.</dd></dl>

Arguments

Author

Gaussian EM-step with random initialisation — em_rinit

<dl>

 <dt>y</dt>
<dd>time series.</dd>

 <dt>order</dt>
<dd>MixAR order, vector of length the number of components.</dd>

 <dt>partempl</dt>
<dd>parameter template, a list containing one element for each mixture
 component, see <code>randomArCoefficients</code>.</dd>

 <dt>etk</dt>
<dd>MixAR component residuals, a matrix.</dd>

</dl>

em_rinit: Gaussian EM-step with random initialisation

Description

Usage

Value

Arguments

Author

Details