Usage
Mstep.nh.MSAR(data,theta,FB,covar=NULL,method=method,
ARfix=FALSE,reduct=FALSE,penalty=FALSE,sigma.diag=FALSE,
lambda1=lambda1,lambda2=lambda2,par = NULL)
Arguments
data
array of univariate or multivariate series with dimension T*N.samples*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
covar
transitions covariates
method
permits to choice the optimization algorithm. default is "ucminf", other possible choices are "BFGS" or "L-BFGS-B"
sigma.diag
if TRUE the innovation covariance matrices are diagonal.
reduct
if TRUE, autoregressive matrices and innovation covariance matrices are constrained to have the same pattern (zero and non zero coefficients) as the one of initial matrices.
ARfix
if TRUE the AR parameters are not estimated, they stay fixed at their initial value.
lambda1
penalization constant for the precision matrices. It may be a scalar or a vector of length M (with M the number of regimes). If it is equal to0 no penalization is introduced for the precision matrices.
lambda2
penalization constant for the autoregressive matrices. It may be a scalar or a vector of length M (with M the number of regimes). If it is equal to0 no penalization is introduced for the atoregression matrices.
penalty
choice of the penalty for the autoregressive matrices. Possible values are ridge, lasso or SCAD (default).
par
allows to give an initial value to the precision matrices.