Mstep.hh.MSAR.with.constraints


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.


data


model&#39;s parameter; object of class MSAR. See also init.theta.MSAR.


theta


Forward-Backward results, obtained by calling Estep.MSAR function


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


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.

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.

Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations.  Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.

Mstep.hh.MSAR.with.constraints:

Description

Usage

Arguments

Value

See Also