SISstep.fading

This function implements one step of the sequential importance sampling method for fading channels.

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Xialu Liu

Nonlinear Time Series Analysis

Ruey Tsay

Rong Chen

SISstep.fading function

<dl><dt>mm</dt>
<dd>the Monte Carlo sample size <code>m</code>.</dd>
<dt>xx</dt>
<dd>the sample in the last iteration.</dd>
<dt>logww</dt>
<dd>the log weight in the last iteration.</dd>
<dt>yyy</dt>
<dd>the observations with <code>T</code> columns and <code>ydim</code> rows.</dd>
<dt>par</dt>
<dd>a list of parameter values. <code>HH</code> is the state coefficient model, <code>WW*t(WW)</code> is the state innovation covariance matrix,
<code>VV*t(VV)</code> is the covariance of the observation noise, <code>GG</code> is the observation model.</dd>
<dt>xdim2</dt>
<dd>the dimension of the state variable <code>x_t</code>.</dd>
<dt>ydim</dt>
<dd>the dimension of the observation <code>y_t</code>.</dd></dl>

Arguments

Sequential Importance Sampling Step for Fading Channels — SISstep.fading

<dl>

<dt>mm</dt>
<dd>the Monte Carlo sample size <code>m</code>.</dd>


<dt>xx</dt>
<dd>the sample in the last iteration.</dd>


<dt>logww</dt>
<dd>the log weight in the last iteration.</dd>


<dt>yyy</dt>
<dd>the observations with <code>T</code> columns and <code>ydim</code> rows.</dd>


<dt>par</dt>
<dd>a list of parameter values. <code>HH</code> is the state coefficient model, <code>WW*t(WW)</code> is the state innovation covariance matrix,
<code>VV*t(VV)</code> is the covariance of the observation noise, <code>GG</code> is the observation model.</dd>


<dt>xdim2</dt>
<dd>the dimension of the state variable <code>x_t</code>.</dd>


<dt>ydim</dt>
<dd>the dimension of the observation <code>y_t</code>.</dd>

</dl>

SISstep.fading: Sequential Importance Sampling Step for Fading Channels

Description

Usage

Value

Arguments

References