Mstep.hh.reduct.MSAR: M step of the EM algorithm for fitting homogeneous Markov...

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive model swith constraints on the matrices, called in fit.MSAR. The matrices are constrained to have the same pattern ()zeros and non zeros coefficients) as the initial matrices.

Usage

Mstep.hh.reduct.MSAR(data, theta, FB, sigma.diag=FALSE)

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
sigma.diag	if TRUE the innovation covariance matrices are diagonal.

Value

A list containing

A0	intercepts
A	AR coefficients
sigma	variance of innovation
prior	prior probabilities
transmat	transition matrix

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, fit.MSAR, Estep.MSAR, Mstep.classif

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