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

Description Usage Arguments Value Author(s) References See Also

View source: R/Mstep.hh.reduct.MSAR.R

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

1
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, valerie.monbet@univ-rennes1.fr

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.