Estep.MSAR.VM: Estep of the EM algorithm for fitting von Mises (non)...

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

Forward-backward algorithm called in fit.MSAR.

Usage

Estep.MSAR.VM(data, theta, smth = FALSE, verbose = FALSE, 
   covar.emis = NULL, covar.trans = NULL)

Arguments

data	array of univariate or multivariate series with dimension T*N.samples*d. T: number of time steps of each sample, N.samples: number of realisations of the same stationary process, d: dimension.
theta	model's parameter; object of class MSAR. See also init.theta.MSAR.
smth	If smth=FALSE, only the forward step is computed for forecasting probabilities. If smth=TRUE, the smoothing probabilities are computed too.
verbose
covar.emis	covariables for emission probabilities.
covar.trans	covariables for transition probabilities

Value

list including

loglik	log likelihood
probS	smoothing probabilities: P(S_t=s|y_0,\cdots,y_T)
probSS	one step smoothing probabilities: P(S_t=s,S_{t+1}|y_0,\cdots,y_T)

Author(s)

Valerie Monbet, [e-mail]

References

Ailliot P., Bessac J., Monbet V., Pene F., (2014) Non-homogeneous hidden Markov-switching models for wind time series. JSPI.

See Also

fit.MSAR.VM, Mstep.hh.MSAR.VM,Estep.MSAR

NHMSAR documentation built on Feb. 9, 2022, 9:06 a.m.