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

Description Usage Arguments Value Author(s) See Also

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

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, [email protected]

See Also

Mstep.hh.MSAR, fit.MSAR


NHMSAR documentation built on Aug. 31, 2017, 9:04 a.m.