IRAOI JOURNAL OF STATISTICAL SCIENCES

This paper presents fits for neural network model , and
comparative resulting forecasts with those obtained from Box-
Jenkins Method. We use time series data of Tigris's monthly flow
into Mosul city from 1950-1995. To perform a comparative .
forecasting work through the Box-Jenkins and neural network
doesn't mean working with two different or competing aspect ;
on the contrary choosing a proper architecture of neural net
works requires using the skills of statistical modeling . As for
application , Box-Jenkins Method has given more appropriate
forecasts than those given by feed forward artificial neural
network . We used Minitab and SPSS programs in the statistical
aspect and Alyuda program in the neural network aspect.

In this paper, we have proposed a modified QN-algorithm for
solving a self-scaling large scale unconstrained optimization
problems based on a new QN-update. The performance of the
proposed algorithm is better than that used by Wei, Li, Yuan
algorithm. Our numerical tests show that the new proposed
algorithm seems to converge faster as compared with a standard
similar algorithm in many situations

In this paper we improve the Karmarkar's method for linear
programming by using the vector of initial point with all
iteration, and when )1n(3
n
−
=θ , we see that the Karmaker's
method can be reduced to a direct method without iteration and
grantee the optimal solution. Finally the new method have been
compared with Karmaker's. The numerical results show that the
new method is better and faster

Bayesian decision theory is a fundamental statistical approach to
the problem of classification as for pattern recognition. It makes the
assumption that the decision problem is posed in probabilistic term,
and all of the relevant probability values are known. To minimize
the error probability in classification problem, one must choose the
state of nature that maximizes the posterior probability. Bayes
formula allows us to calculate such probabilities given the prior
probabilities, and the conditional densities for different classes or
categories.
Statistical classification is one of the most useful analysis tool
which can be used for analyzing several kinds of data in various
sciences. Its interesting in separating more than one class or
category from each other, when their behaviors are near each other.
The Bayesian surface decision equation is one of the classification
rule that produces a linear separable equation for these interacted
classes, specially where they contain vectors of random variables
that are identically independently distributed (iid),(or all vectors have the same distribution with same parameters values). This was
done as a special case by Muller, P. & Insua, D.R, (1995), This
study is a trial to generalize (Bayesian Surface Decision Equation)
to produce a such (Linear Separable Equation) as a linear classifier
for those classes contain random vectors distributed identically
Gaussian) but with different parameters values ( mi , Σ i ),
moreover, in this study the researcher tried to search for equivalence
between Bayesian Surface Decision Equation, and a linear
perceptron for classification for this general case, with a numeric
application, (encoded data vectors) that is illustrated latter.

This research aims to finding the shortest path between two Centers S
and T which are connected by a network of roads of certain length and
certain material or time costs, (as the word Centre could be cities or storage
depots or production centers or centers of import and export) by using the
new proposed programmable algorithm which depends on converting the
road network, to the matrix in which number of columns and rows is equal
to the number of the arcs of the under-study network (What ever the size of
the road network of the studied problem is), and its elements are: either (1)
which means that there is an arc connecting the two centers, or (0) which
means that there is no arc connecting the two centers, and then processing
this matrix in three basic stages:
First: the stage of identifying the number of paths in the network: by
counting the number of elements that are equal to (1) in the first row of the
matrix, which represents the initial number of tracks, then moving to the
following lines and counting the number of elements that is equal to (1) if
they are more than one we will have other paths their number is equal to
the initial number of elements that is equal to (1) minus one, and add this
number to the number of tracks, and so on.
Second: the stage of limiting the number of arcs, which is embedded
in each path by identifying the number of the line and column of the
elements that are equal to (1).
Third: The stage of counting the lengths of the paths according to the
arcs embedded in each path and comparing them to determine the shortest
one

Statistical inference is based generally on some estimates
that are functions of the data. Resampling methods offer strategies
to estimate or approximate the sampling distribution of a statistic. In
this article, two resampling methods are studied, jackknife and
bootstrap, where the main objective is to examine the accuracy of
these methods in estimating the distribution of the regression
parameters through different sample sizes and different bootstrap
replications

In this brief research we use graph theory, which is
considered as one of the theories of Operations Research, we
give a brief account of what this theory and its subsidiaries and
their importance, their uses and one of the discussions dealt with
the use of such theory in the field of management is to find a
solution to the issue of timetables note that the timelines are not
limited to the administrative side, but also intervention in many
areas as well .
The issue of tables of academic lectures has been chosen
because it is the closest to the perception in the university where
a number of faculty members are distributed to number of rows
to give lectures to these rows in order to achieve the requirement
of teaching in the curriculum and during the specified period of
time to achieve the condition that there is no more than one
teacher to the same row at the same time and also there should
be no more than a lecture to the same teacher at the same time.
After the application of graph theory and using two methods,
including two color method and less tree generator method we
can obtain a timetable to achieve the required conditions. These
two methods are clarified by giveing a simple example was
obtained timetables for appointments, although the difference in
the results of both methods was very simple and both solutions
were investigating conditions of the issue

This research is dedicated to study non-stationary time series ,
and the ability of using fuzzy logic in order to improve
forecasting. The non-stationary time series (Mixed autoregressive
and moving average model) has been linked with fuzzy logic in
order to get on the parameters of fuzzy time series models (Fuzzy
mixed autoregressive and moving average model), and applied
on monthly purchases rates data and foreign currency sales
(Dollar) for the daily bid of Iraqi central bank. The fuzzy mixed autoregressive and moving average model for time series gave
more appropriation forecasting than the forecasting given by
fuzzy mixed autoregressive and moving average model

In this paper, we propose a number of families of
estimators of ratio and regression to estimate the population
variance 2
y
S using auxiliary information in two phase sampling,
the bias and the mean square error expression for the above
estimators are obtained up to terms of order )( 1−
nO only and
calculate minimizing the Mse for all estimators and also the
generalization of estimators

The operation of order estimation of a Markov chain to represent
the observation chain is an important problem in realistic
applications. One method of order estimation depending on
intelligence technicality is represented by artificial neural
networks . in this research we design an artificial neural network
which is the backpropagation error (BPE) . This research treated realistic problem in Markov chain order
estimation to the weather state of raining months in Nineveh
Governorate ( clearly, cloudy, rainy) , and a special algorithm has
been prepared for training the network .We suggest another
neural network for backpropagation error which is forecasting
the weather state , which is designed depending on the past
network after modify it.

In this paper many state space models are fitted. The
statistical criteria used to choose the best estimated model whose
parameters estimated by the Ridge regression method. This
method can be used when the predicator variables are
multicollinear.The data used in this paper represented by the
temperature, which is the response variable and the predictor
variables effulgence, wined, sunshine and evaporation.

More than three years of measurements of aerosol size-distribution
and different gas and meteorological parameters made in a specific region
were analyzed for this study to examine which of the meteorological and
trace gas variables effect on the emergence of nucleation events. As an
analysis method, we used discriminate analysis with non-parametric density
estimation method. The best classification results in our data was reached
with the combination of relative humidity ozone concentration and a third
degree polynomial of radiation. RH appeared to have a preventing effect on
the new particle formation whereas the effects of O3 and radiation were
more conductive. The concentration of SO2 and NO2 also appeared to have
significant effect on the emergence of nucleation events but because of the great amount of missing observation, we had to exclude them from the final
analysis

This paper deals with the proplems of contamination and
heteroscedasticity , by using a method (the Direct Wavelet Filter
that used which some method of thresholding) and comparing it
with some classical method of transformation in the complete
randomized design , through a Matlab code that perform the
filtering process , also SPSS was also used

In this paper we describe single exponential smoothing
method, which is used in time series forecasting, and suggest an
improving to the single exponential smoothing method through
adding the mean of the first differences for the time series for all
predicting values of the single exponential smoothing. The
improved method was compared with single exponential
smoothing method by using real time series data for wheat
national production for the period (1961-2002) through
depending on Cumulative Forecasting Error (CFE), Mean
Absolute Deviation (MAD), Mean Square Error (MSE), and
Mean Absolute Percentage Error (MAPE) as criteria for
comparison. It is clear that the improv method was more efficient
than the single exponential smoothing method for forecasting in
time series.

The need for control systems exists in many fields, such as,
medicine, economics, engineering, agriculture, etc., as a result of
the rapid progress in science and technology in all fields which
cause a creative interaction between control theory and these
fields, especially, in the field of computer science.

Clustering is considered as one of the most scientifical
developments which the scientists reached at in the field of
recent knowledge and technologies to discover the cluster's
group. The clustering concept was introduced firstly by Ronald
in 1955. The clustering's fundamental notion is represented in
dividing the data into clusters. This research aims to using
clustering for actual data modeling for the monthly salary grade
of the teaching staff for one of the Mosul University's College in 2009, by using HCM algorithm to these data. Matlab software is
used to write down the proposed algorithm programs. Results
proved the efficiency of this algorithm in clustering the actual
data and how to represent them as clusters

In this paper, the solution of Symmetric Fuzzy
Quadratic programming is considered by using fuzzy
values with special Phases of Van De method , For the
founded model ,we use the Kuhn Tucker conditions for
solving the Quadratic programming by implementing
the computer program (WINQSB) also by transforming
the model to two models one of them is Linear and the other is Quadratic , of the Matlab in order to check the
results. Finally, the results are very encouraging