R: Sequential Importance Sampling Step for Fading Channels

SISstep.fading {NTS}

R Documentation

Sequential Importance Sampling Step for Fading Channels

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

SISstep.fading(mm, xx, logww, yyy, par, xdim2, ydim)

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

The function returns a list with the following components:

`xx`	the new sample.
`logww`	the log weights.

Tsay, R. and Chen, R. (2018). Nonlinear Time Series Analysis. John Wiley & Sons, New Jersey.

[Package NTS version 1.1.3 Index]