M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive models.

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Description

M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive models, called in fit.MSAR.

Usage

1
Mstep.hh.MSAR(data, theta, FB)

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

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

fit.MSAR, Estep.MSAR, Mstep.classif

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