Mstep.hh.MSAR.with.constraints: 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 constraints on VAR models, called in fit.MSAR. Maximum likelihood is used. Matrices A and sigma are diagonal by blocks.

Usage

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

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
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.

Value

A0	intercepts
A	AR coefficients
sigma	variance of innovation
prior	prior probabilities
transmat	transition matrix

Author(s)

Valerie Monbet, [e-mail]

See Also

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

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