M step of the EM algorithm for fitting homogeneous multivariate Markov switching auto-regressive models with penalization of parameters of the VAR(1) models.

Description

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).

Usage

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

Arguments

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

Value

A0

intercepts

A

AR coefficients

sigma

variance of innovation

sigma.inv

inverse of variance of innovation

prior

prior probabilities

transmat

transition matrix

Author(s)

Valerie Monbet, valerie.monbet@univ-rennes1.fr

See Also

Mstep.hh.MSAR, fit.MSAR

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.