Mstep.classif: fit an AR model for each class of C

Description Usage Arguments Value Author(s) See Also Examples

Description

fit an AR model for each class of C by maximum likelihood method.

Usage

1

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.

C

Class sequence

order

order of AR models (all models will have the same order)

Value

list containing

A0

intercept

A

AR coefficients

sigma

variance of innovation

LL

log likelihood

Author(s)

Valerie Monbet, valerie.monbet@univ-rennes1.fr

See Also

fit.MSAR

Examples

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	data(meteo.data)
data = array(meteo.data$temperature,c(31,41,1)) 
k = 40
T = dim(data)[1]
N.samples = dim(data)[2]
d = dim(data)[3]
order = 2
C = array(meteo.data>0,c(31,41,1))
res = Mstep.classif(data,C,order=order)
str(res)


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