Mstep.nn.MSAR: M step of the EM algorithm.

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

M step of the EM algorithm for fitting Markov switching auto-regressive models with non homogeneous emissions and non homogeneous transitions.

Usage

Mstep.nn.MSAR(data, theta, FB, 
   covar.trans = covar.trans, covar.emis = covar.emis, method = 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.
FB	Forward-Backward results, obtained by calling Estep.MSAR function
covar.trans	transitions covariates
covar.emis	emissions covariates (the covariates act on the intercepts)
method	permits to choice the optimization algorithm. default is "ucminf", other possible choices are "BFGS" or "L-BFGS-B

Value

A0	intercepts
A	AR coefficients
sigma	variance of innovation
prior	prior probabilities
transmat	transition matrix
par_emis	emission parameters
par.trans	transitions parameters

Author(s)

Valerie Monbet, [e-mail]

References

Ailliot P., Monbet V., (2012), Markov switching autoregressive models for wind time series. Environmental Modelling & Software, 30, pp 92-101.

See Also

Mstep.hh.MSAR

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