Mstep.hh.ridge.MSAR: M step of the EM algorithm for fitting homogeneous...

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive 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

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, [e-mail]

See Also

Mstep.hh.MSAR, fit.MSAR

NHMSAR documentation built on Feb. 9, 2022, 9:06 a.m.