IRAOI JOURNAL OF STATISTICAL SCIENCES

In this paper,a new extended (CG) algorithms is proposed. It
is in fact, a particular type of the Conjugate Gradient (CG)
method which employs non-quadratic rational model, and based
on inexact line searches. The Flecther and Reeves restarting
criterion was employed to the standard and New versions and
gave dramatic savings in computational time. The new
algorithms is were promising in general, seven non linear tests
function with different versions were used

A condensed study was done to compare between the
ordinary estimators. In particular the maximum likelihood
estimator and the robust estimator, to estimate the parameters of
the bivariate mixed model of order one, namely BARMA(1,1).
Simulation experiments were done for varieties of
BARMA(1,1), using small, moderate and large sample sizes,
where some new results were obtained. MAPE was used as a
statistical criterion for comparison.

Classification is an important field with many applications. In
particular, the classification of digital imagery has important
applications in the mapping community. In this paper the
comparison of three different classification methods on LANDSAT
imagery of Erbil City - IRAQ: neural networks, nearest-neighbor,
and discriminant analysis are made.
Out of the three approaches, k-nearest neighbors performed the
best; next in accuracy was neural networks, and then discriminant
analysis.

This paper includes a simulation study of three well-known
filters in real lift application. These filters are - filter, Kalman
filter and FIR filter. Noisily realizations were generated from
three stochastic models and depending on three different families
of probability distribution of noise. The modern computer
application MATLAB 6 is used . The simulation results indicated
that FIR filter is more powerful than the other two filters.

This research is focusing on methods related to smoothing
Nonparametric Regression Functions. This is for the purpose of
producing the best methods convenient for various methods.
Thus, the most important purpose of the research is to find what
the studies so far have offered in the field of Nonparametric
Regression. Also to find alternative or modified methods, which
are reliable for the treatment of failure regarding the methods in
use, for example for the treatment of failure for penalized
shrinkage method which depends on truncated Polynomial
Regression spline model, especially for special models ,we
suggest modification of knots make method more efficiency A simulation model has been performed with different
distributions, for a number of methods. To verify the
performance of such methods, many criteria have been carried
out.

Forecasting of future behavior of time series is one of the
important subjects in statistical sciences, because of its important
need in different areas of life. Interest in forecasting had
increased in the recent years, and some new techniques in the field of computer science, like Artificial Neural Networks
(ANN), had appeared. These techniques have the ability learning
and self- adaptation with any model, and don’t need assumptions
on the nature of time series. On the other hand the classical
forecasting methods, like Box-Jenkins method, need hard
conditions. Hence, the need for a comparison between classical
methods in forecasting of time series with ANN technique arose.

In this paper we study the migration of population among
the three Iraqi cities: Nineva , Saladean, Tameem by considering
these this migrations as a Markov chain with three states which
represent the transitions among these cities .We obtain a
transition matrix (3x3) to represent these states and we obtain
that this chain is Ergodic and we find the mean and the variance
of the waiting time ,the time between consecutive transition and
the duration of an excursion at each state.

The objective of this research is to arrive at to the
following
- Determine the number of factors that are extraced after using
factor analysis for different dry milk type .
- Determine number of different Clustering groups .
- Explain the relationship between the elements after using the
two analysis together .
The research depends on (20) samples drown randomly from
deferent dry milks . Using the two analysis Factor and
Cluster analysis and by using statistical software (SPSS),
and the results are:
First: Cluster analysis
There are two factors which explain 91.8% of the total of
variation between variables was used in this research . The first
eigen value was 52.3% of the total variation , while the second
eigen value was 38.7% of the total variation .
Second: Factor analysis
Three groups appear as a result of cluster analysis using
data of (20) samples of dry milk types , as follows :
A- The first group involves full cream milk .
B- The second group involves also full cream milk ,
C- The third group involves infant formula .
Third : The two analysis together
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Design figure (2) which consists of two dimensions
depending on figure (1). Matching the results of the two
analysis, where the cluster analysis classifies the different
samples of dry milk types into three groups (as mention above).
while the factor analysis mentions that the two groups (first and
second) waswere two closet and they are far away from the
third group . this reflects the nature of the structure of these
samples, where the two groups(first and second) involve full
cream milks , while the third group involves infant formula

Most of the time series that appear in many economical
geophysical and otherphenomenas are driven by non-Gaussian
random error (a t ), so the aim of this paper is to investigate some
of the probabilistic properties of Gaussian and non-Gaussian
mixed model ARMA(1,1), and the identification methods of this
model.
The researchers have theoretically derived the characteristic
function the first four moments and the skeweness and Kurtosis
coeficients for (a t ) for Gaussian distribution and non-Gaussian
distribution (poisson) and simulation experiment were used to
confirm the accuracy of the theoretical results, We have also
declared the identification sample autocorrelation function
(ESACF) and (Kumar) method (c-table) depending upon the
pade approximation and we have suggested a method depending
upon the extended sample partial autocorrelation function
(ESPACF) to ascertain the efficiency of suggested method.

In this paper the radial basis function method is used
for the generation of models of time series. It has been arrived
at a model for nonlinear time series. It has also been found that
the error is restricted between zero and one. The probability
characteristics have been explained for such series
accompanied by a complete analysis for the residuals of
model. A simulation has been done for the purpose of
examination the generative information of the model. which
have the same information characteristics.

The development of life has made tons of documented data
available and with the wide working of banks administration, the
method of recognizing character is found.The aim of this research is to discover a method to
recognize the English Printed character to take a text by using the
scanner, then recognize the characters. We used the boundary
algorithm to perform the segmentation and obtain the character,
and then the following algorithm is used for feature extraction.
The major work focused on the recognition of English
Printed-Characters which are obtained by scanner, a little number
of characters were not recognized compared with other samples
which are recognized.

This paper deals with the problem of estimating parameters
of spatial quadratic model by Bayesian technique. This technique
involves the prior information of the first and second moment of the parameters. This estimation model is called the Bayesian quadratic
unbiased estimator, which is linear in the parameters. The results of
estimation are compared with the estimates of minimum norm
quadratic unbiased estimators and the results are encouraging.
All algorithms of computation are written by using MatLAB
programming.