The M step contains two parts. One for the estimation of the parameters of the hidden Markov chain and the other for the parameters of the auto-regressive models. A numerical algortihm is used for the emission parameters.
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.
FB
Forward-Backward results, obtained by calling Estep.MSAR function
covar
emissions covariates (the covariables act on the intercepts)
verbose
if verbose is TRUE some iterations of the numerical optimisation are print on the console.
Value
List containing
..$A0
intercepts
..$A
AR coefficients
..$sigma
variance of innovation
..$prior
prior probabilities
..$transmat
transition matrix
..$par_emis
emission parameters
Details
The default numerical optimization method is ucminf (see ucminf).
References
Ailliot P., Monbet V., (2012), Markov switching autoregressive models for wind time series. Environmental Modelling & Software, 30, pp 92-101.