Sstep.Smooth.Sonar

This function uses the sequential importance sampling method to deal with a target with passive sonar for smoothing.

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

Sstep.Smooth.Sonar function

<dl><dt>mm</dt>
<dd>the Monte Carlo sample size <code>m</code>.</dd>
<dt>xxt</dt>
<dd>the sample in the last iteration.</dd>
<dt>xxt1</dt>
<dd>the sample in the next iteration.</dd>
<dt>ww</dt>
<dd>the forward filtering weight.</dd>
<dt>vv</dt>
<dd>the backward smoothing weight.</dd>
<dt>par</dt>
<dd>a list of parameter values. <code>H</code> is the state coefficient matrix, and <code>W*t(W)</code> is the state innovation covariance matrix.</dd></dl>

Arguments

Sequential Importance Sampling for A Target with Passive Sonar — Sstep.Smooth.Sonar

<dl>

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


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


<dt>xxt1</dt>
<dd>the sample in the next iteration.</dd>


<dt>ww</dt>
<dd>the forward filtering weight.</dd>


<dt>vv</dt>
<dd>the backward smoothing weight.</dd>


<dt>par</dt>
<dd>a list of parameter values. <code>H</code> is the state coefficient matrix, and <code>W*t(W)</code> is the state innovation covariance matrix.</dd>

</dl>

Sstep.Smooth.Sonar: Sequential Importance Sampling for A Target with Passive Sonar

Description

Usage

Value

Arguments

References