MKF.Full.RB: Full Information Propagation Step under Mixture Kalman Filter

In NTS: Nonlinear Time Series Analysis

Full Information Propagation Step under Mixture Kalman Filter

Description

This function implements the full information propagation step under mixture Kalman filter with full information proposal distribution and Rao-Blackwellization, no delay.

Usage

MKF.Full.RB(
  MKFstep.Full.RB,
  nobs,
  yy,
  mm,
  par,
  II.init,
  mu.init,
  SS.init,
  xdim,
  ydim,
  resample.sch
)

Arguments

MKFstep.Full.RB	a function that performs one step propagation under mixture Kalman filter, with full information proposal distribution. Its input includes (mm,II,mu,SS,logww,yyy,par,xdim,ydim), where II, mu, and SS are the indicators and its corresponding mean and variance matrix of the Kalman filter components in the last iterations. logww is the log weight of the last iteration. yyy is the observation at current time step. It should return the Rao-Blackwellization estimation of the mean and variance.
nobs	the number of observations T.
yy	the observations with T columns and ydim rows.
mm	the Monte Carlo sample size m.
par	a list of parameter values to pass to Sstep.
II.init	the initial indicators.
mu.init	the initial mean.
SS.init	the initial variance.
xdim	the dimension of the state varible x_t.
ydim	the dimension of the observation y_t.
resample.sch	a binary vector of length nobs, reflecting the resampling schedule. resample.sch[i]= 1 indicating resample should be carried out at step i.

Value

The function returns a list with components:

xhat	the fitted value.
xhatRB	the fitted value using Rao-Blackwellization.
Iphat	the estimated indicators.
IphatRB	the estimated indicators using Rao-Blackwellization.

References

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

NTS documentation built on Sept. 25, 2023, 1:08 a.m.