mix_hatk

mix_hatk-methods

mix_hatk,MixAR,numeric,numeric,missing-method

Function and methods to compute component predictions for MixAR models

methods

MixAR

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

mix_hatk function

<dl> <dt>model</dt>
<dd>a model.</dd> <dt>x</dt>
<dd>time series.</dd> <dt>index</dt>
<dd>a vector of positive integers specifying the indices for which to
 compute the residuals, has a natural default.</dd> <dt>xcond</dt>
<dd></dd>the past values needed for the conditional distribution, a numeric
 vector of length at least the maximal AR order of the components.
</dl>

Arguments

Author

<dl> <dt><code>signature(model = "MixAR", x = "numeric", index = "numeric",
 xcond = "missing")</code></dt>
<dd></dd>
</dl>

Methods

Function and methods to compute component predictions for MixAR models

Compute component predictions for MixAR models — mix_hatk

<dl>

 <dt>model</dt>
<dd>a model.</dd>

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

 <dt>index</dt>
<dd>a vector of positive integers specifying the indices for which to
 compute the residuals, has a natural default.</dd>

 <dt>xcond</dt>
<dd>

the past values needed for the conditional distribution, a numeric
 vector of length at least the maximal AR order of the components.
</dd>

</dl>

<dl>

 <dt><code>signature(model = "MixAR", x = "numeric", index = "numeric",
 xcond = "missing")</code></dt>
<dd></dd>


</dl>

mix_hatk: Compute component predictions for MixAR models

Description

Usage

Arguments

Author

Methods

See Also