IRAOI JOURNAL OF STATISTICAL SCIENCES

One of the most important problems in linear reservoir is the distribution of the
storage.The main goal of this paper is to find the distribution of the outflow of linear
reservoir and then the storage distribution.This is done by applying a simple technique
which is the Fast Fourier Transformation (FFT) Method.

Modeling count variables is a common task in many application
areas such as economics, social sciences, and medicine. The classical
Poisson regression model for count data is often used and it is limited in
these disciplines since count data sets typically exhibit overdispersion, so
negative binomial regression can be used. We use a jackknife- afterbootstrap procedure to assess the error in the bootstrap estimated
parameters. The method is illustrated through two real examples. The
results suggest that the jackknife- after- bootstrap method provides a
reliable alternative to traditional methods particularly in small to
moderate samples

Passing the chosen diagnosed candidate model is considered an
employed modeled gate and the possibility of data presentation to be
used in prediction and control. Also
the research has dealt with different tests for Portmanteau and because
the wide spread test in the ready made Programs is the L-B test, and sicne many studies mentioned that this test suffers from huge differences
in variation this led us to deal with the most recent tests like H-M and LM trying to study its trial empirical power and its statistical specification
(mean, variance) and applying some modification through adopting the
partial autocorrelation coefficient PACF instead of the autocorrelation
coefficients ACF, when calculated and suggesting a new test depending
on the mean of the autocorrelation coefficient -rk- that was suggest by
Moran 1948 .
To reach this target simulation technique was used to
comparieson Portmanteau tests in addition to the suggested test .The
study showed that the use of the PACF in portmanteau tests contributed
to increase it's empirical power and at the same time decrease it's
variance.

Support vector machine initially developed to perform binary
classification. This paper presents a multi-class support vector machine
classifier and ordinal regression to classify the type of bone mineral
density. This paper compares the performance of four multi-class
approaches, one-against-all, one-against-one, Weston and Watkins, and
Crammer and Singer. Results from our real life data conclude that
Crammer and Singer may be better approach depending on training error
and the percentage of correctly classified test data. Also, we find that the
training error becomes more less when the regulization parameter C and
kernel parameter σ become large.

Three statistical analyses were applied: analysis of variance and then
the multistatistical comparisons represented by the test of Newman-Keul,
the quality control procedure and the cluster analysis, to compare between
the results of their analyses on the data represented a chemical
specification by water hardness (ml/l) of Tigris river in Mosul town. The
samples were taken from five sites which are: Darnajook, Shouhada'a
bridge, Nineveh bridge, Danadan and Yaremjah.
Some matched results were noticed between Newman-Keul test and the
quality control procedure, while cluster analysis gave different results.

The subject of relying on forecasting models which depend on
time series has become prominent recently especially with the
easy using of those models , according to application package,
The search harmonized many of these models and advanced
methods for the gaining of accurate forecasting to an important
and active indictor for the education in universities this is the
average of harmonization , as it is important to know the
development of the indictor to build its capacity in that field to emerge to in the quality and reliance also the research involves
other indictors for education at universities like students
admission accepting . Then comparison Was made between two
various models, the method of index exponential smoothing and
Box & Jenkins then select the best of them for forecasting to hose
indictors , as Box & Jenkins models excelled relying on the
comparison criteria (MSE)

Prediction of time series is the most important and widest spread for researcher
nowadays, for it's importance in different approaches ,especially study of natural
phenomena .This study deals with the prediction of fuzzing pattern matching
models which use three algorithms;(Singh,2001) algorithms ,(Altai,2010)
algorithms ,while the third algorithm is an evolution algorithm which combined the
two algorithms, in addition we make some correction which leads to give a better
results than both algorithms and also the prediction of multiple time series models
used which called transfer function models .

This paper is concerned with fitting some black box models. Some of
them are, the outputs error model which contains the autoregressive and
autoregressive moving average with additional inputs(ARX and
ARMAX).The best model has been chosen which represents the data
about the Temperature which is affected by some predictor variables
which they were represented by (Brightness, unlike radiation, and
evaporation).The parameters of the best model were estimated by the
ridge regression method with and without the existence of prior
information around the model parameters. The prediction errors of the
model which has been estimated by least square and ridge regression
when the prior information about the parameters is available were
compared.

The research is a comparison of solving some game theory
problems by using the genetic algorithm and LINGO software
package to define the best one in solving such problems. The research
concluded that the least value of the match was accomplished by
using genetic algorithm in contrast with solving by LINGO . The
time ratio by which every player can use the available strategy to
achieve the optimum value of the match was also defined

The revolutionary technology of digital communications
delivered more pressure on many public sector institutions to
convert their operations to the world of e-business is known as
electronic government, known as e-government as including the
integrative effective use for all information and communication
technologies to facilitate the administrative processes of daily
government sectors. The vital features that can be able to
experience the people and between those vital features (fingerprints, face, hand geometry, iris scan). This research aims
to use iris scans to identify people through the image of the irisand the application program using the package (MATLAB)
to match the images iris for people as it has matching images
with the images stored in the database if the amount of the
difference between the two images was equal to zero the number
of the compared images that were compared It is compared to all
the images were obtained a high percentage of the corresponding
images.

The main objective of the statistical methods address the issue of predictability
and quality control within the required quality standards and take advantage of the
capabilities of computer in the preparation of a computer program to accomplish the
tasks of our study, hence this research of a prediction for the testing of water in a Box
– Jenkins method for its ability to deal with non-stable time-series and even nonlinear and then calculating the rates of the tests, projects and control in Fuzzy logic .