M step of the EM algorithm.

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Description

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

Usage

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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, 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

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