Description Usage Arguments Value Author(s) See Also

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

M step of the EM algorithm for fitting homogeneous multivariate Markov switching auto-regressive models with penalization of parameters of the VAR(1) models, called in fit.MSAR. Penalized maximum likelihood is used. Penalization may be add to the autoregressive matrices of order 1 and to the precision matrices (inverse of variance of innovation).

1 | ```
Mstep.hh.ridge.MSAR(data, theta, FB,lambda)
``` |

`data` |
array of univariate or multivariate series with dimension T x N.samples x 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 |

`lambda` |
penalisation constant |

`A0` |
intercepts |

`A` |
AR coefficients |

`sigma` |
variance of innovation |

`sigma.inv` |
inverse of variance of innovation |

`prior` |
prior probabilities |

`transmat` |
transition matrix |

Valerie Monbet, [email protected]

Mstep.hh.MSAR, fit.MSAR

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