الفهرس 
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Abstract
It is well known that time series inalysis u5ually becomes much more complicated whi a it L
account of superimposed observational ea
suppocd the superimposed error as disc
(1944)
.ome times it is impossibl to obs
which is described by a linear siationar.
take into account the error of tite obser
be for example the results of th instn
[n this work we shall cone der the
proce&..es xc..t) which are, the ac oregr&
movin average MA(q) process and mixed t
moving average ARMA. (p,q) proce& .. In e
suppose it is Gaussian process wLth mesi
examine the likelihood estimatic of th’.
these kinds of processes with er’or, fo
conditions (Eee L1) the likeli ±ood eqw.tion has a solution which converges in proLabilit to the true value of the parameter.
It wil] be assumed that the error is a sequence of Gaussian random variables with mean zer and variance We assume Lnat the two processes X(t) , t) are independent.
necessary to
or. The problem ssed by Kandell
ye the process nodel. V’e must tions which may nts.
he originaly
‘ze process ARC;) oregressive
y case we shall crc. We shall
.arameters of rider certain