Generic sequential Monte Carlo using full information proposal distribution.
SMC.Full(
SISstep.Full,
nobs,
yy,
mm,
par,
xx.init,
xdim,
ydim,
resample.sch,
delay = 0,
funH = identity
)The function returns a list with the following components:
the fitted values.
the log-likelihood.
a function that performs one step propagation using a proposal distribution.
Its input includes (mm,xx,logww,yyy,par,xdim,ydim,resample), where
xx and logww are the last iteration samples and log weight. yyy is the
observation at current time step. It should return xx (the samples xt) and
logww (their corresponding log weight), resample is a binary value for resampling.
the number of observations T.
the observations with T columns and ydim rows.
the Monte Carlo sample size m.
a list of parameter values to pass to Sstep.
the initial samples of x_0.
the dimension of the state varible x_t.
the dimension of the observation y_t.
a binary vector of length nobs, reflecting the resampling schedule. resample.sch[i]= 1 indicating resample should be carried out at step i.
the maximum delay lag for delayed weighting estimation. Default is zero.
a user supplied function h() for estimation E(h(x_t) | y_t+d). Default
is identity for estimating the mean. The function should be able to take vector or matrix as input and operates on each element of the input.
Tsay, R. and Chen, R. (2018). Nonlinear Time Series Analysis. John Wiley & Sons, New Jersey.