Description Usage Arguments Value Author(s) See Also

View source: R/Mstep.hh.MSAR.with.constraints.R

M step of the EM algorithm for fitting homogeneous multivariate Markov switching auto-regressive models with constraints on VAR models, called in fit.MSAR. Maximum likelihood is used. Matrices A and sigma are diagonal by blocks.

1 | ```
Mstep.hh.MSAR.with.constraints(data, theta, FB, K, d.y)
``` |

`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 |

`K` |
number of sites. For instance, if one considers wind at k locations, K=k. Or more generally number of independent groups of components. |

`d.y` |
dimension in each sites. For instance, if one considers only wind intensity than d.y = 1; but, if one considers cartesian components of wind, then d.y =2. |

`A0` |
intercepts |

`A` |
AR coefficients |

`sigma` |
variance of innovation |

`prior` |
prior probabilities |

`transmat` |
transition matrix |

Valerie Monbet, [email protected]

Mstep.hh.MSAR, fit.MSAR, Mstep.hh.SCAD.MSAR

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