IRAOI JOURNAL OF STATISTICAL SCIENCES

In this paper, I considered the problem of selection a model from a
collection of candidate models specified by multiple linear regression model.
Two criteria are used and compared, they are Akaike information criterion
( ) and Bayesian information criterion ( ). A real data set is
considered as an application case. I preferred the model that the chosen
rather than depending on the values of and the .

In this paper we derive Bayes' estimator for the Scale
parameter θ in Gamma distribution when α is known and equal
2, i.e. ),2(Ga~,...,, n21 θΧΧΧ , we take 2=α to estimate one
parameter of gamma distribution which is θ (Scale parameter),
where gamma distribution is considered as an important model of
the life time models . These estimators are obtained depending on
squared error and LINEX loss function , Then comparisons of
risks for θ under squared and LINEX loss function have been
made . Simulation study is given to illustrate that the proposed
estimators LB
ˆθ is preferable to SB
ˆθ for the sample sizes 30,20,10n =
from above distribution with parameters )1,2( =θ=α and for all
values of " a " )2,1,5.0a( ±±±= .

In this research, we suggest an algorithm to select the
initial centers of clusters; this algorithm includes the selection of
features of different attributes as an initial center of each cluster.
As a fruit of this algorithm it gives a unique cluster when
processes any group of data many times. This result leads to save
time and effort. Simulation experiments performed to test the
proposed algorithm. Also, a real data deals with the food security
of Arabic World is applied.

With regard to the difficulty and the big role that is made to
reach the optimal solution of the institutions and factories system
processing through the perfect or alternative decision making
among the abundant decisions or alternative groups, and since
that the information could be inaccurate or uncertain , in this
research one of the types of non–linear logistic functions has
been used, that is the modified s- curve membership function in
the selection of the best production mixture for solving the
problems of industrial institutions through the application of
fuzzy linear programming method.
This function is qualified by including an important
factor(fuzzy factor ) which could determine the shape of function
as well as by its flexibility in dealing with fuzzy indications.
The industrial production units face the problem of
being fuzzy in their various areas such as raw materials , human
resources , work hours , …,etc. In order to solve this problem
,fuzzy programming method has been applied in this research in
“the General Company for Vegetarian Oil ” to determine the best
mixture and to achieve the required object increasing the
profitability according to two important factor . the first factor is
the level of satisfaction by taking (21) level which ranged
between (0.0010-0.999) with an excess (0.0499) . The second
one is fuzzy factor that ranged between (1-40) with an excess (2)
that each one of them has been determined by the researcher .
The research has concluded that the optimal decision
depends on the fuzzy Factor in the problem of determining the
production mixture in the fuzzy model , as well as that the
highest level production units could be obtained when the
fuzziness in the model is low

This paper is concerned with the estimation of the
parameters of Weibull distribution whose density function is
given by
( )[ ] 0,0,0,exp),;( 1 >>≥−= −
β
α
χ
αχ
χ
αβ
β
αχ
β
β
β
w
Samples for each of the different sample sizes were simulated
1000 times computer simulation is used to obtain the statistical
properties of the estimators .
Estimators have been obtained by method of least squares,for
the two cases, presence and absence of the second order
approximation in the Taylor expansion, and comparison between
the two cases is made. The estimators is less biased for the case
where second approximation in Taylor expansion is present than
for other case.
In order check the model examination of the residual is
carried out for a sample of size 50 with 5 simulations for both in
case of presence and absence of second order approximation in
the Taylor expansion.
The errors have zero mean and constant variance.
The calculation is carried out by computer

In this research, the parameter was estimated for Gibbs
random distribution using different energy functions. We get the
first algorithm by adding noise to the image and restore it by
filters. We obtain the second algorithm by degradation of the
image and restore it to the original image or close to it. The third
algorithm has energy function which depends on the mean and
standard deviation.

In this paper, we use several ways to distinguish between the
deterministic time series and stochastic white noise by using
correlation exponent test and the BDS test ( Brock Deckert Scheinkman ) ,through simulating a large number of data for
testing the efficiency of these methods on several different
models.
We found that the correlation exponent tests can distinguish
white noise from chaos, however it can not distinguish a white
noise from a chaotic process mixed with small a mount of white
noise .
By putting a white noise as the null hypothesis the BDS test
rejects the null hypothesis when the data came from from
stochastic source.

We study a new technique of forecasting time series , this is
technique is pattern matching Fuzzy, which identify past
relationships and its trends in historical data for forecasting
future values .We connect Fuzzy Pattern Matching model with
time series and we also make algorithm of Fuzzy Pattern
Matching and application it on the real data (consuming electric
energy in Ninevah Governerate) , and we used Mean Squared
Deviation (MSD) and Mean Absolute Deviation (MAD) to get
the optimal values

The research deals with the topic of variable selection in
linear regression using robust procedures as resistant to outliers
and other failures of assumptions . The objective of the research
is using robust criteria (RAIC , RSIC, RC p, RVC, RSC, RF, RAPE) in selection of most adequate independent variables in the
regression model used to estimate blood sugar as dependent
variable and other independent variables and comparing the
performance of these criteria. The results shows that the RAPE
criterion was the best in selecting the most important variable
compared with the other criteria.

This paper proposed a new algorithm for solving a
nonlinear integer programming for human resources allocation
problem to find an optimal solution. It has been concluded that
this algorithm is relatively simple and efficient for solving this
type of problems, if it is compared to the other traditional
approaches such as Kuhn-Tucker conditions and Lagrange
multipliers method.

The main objective of this research is to design a program
model for a new product density estimation by implementing
fuzzy logic techniques. This model is designed depending upon
some of the factors influencing product density. The model
consists of conditional rules. Mamdani fuzzy inference system is
used for reasoning process because it is an efficient type of fuzzy
inference for knowledge to make decision processing. The
model is designed using MATLAB as the programming tool for
writing the model's programs. The model is applied to real data
average taken from Mosul factory for children Ready-Made
clothes. The results obtained proved that FLPDEM is an
attractive model for new product density

In this research, we consider the study of conditional
expectation and it's relationship with regression model. The conditional expectation has a linear form which is specified as a
simple linear regression model. The power transformation was
used on the predictor variable which gave the best possible fit for
the model which was derived from the binomial distribution
conditioned with varying sampl size.
The parameters of specified models were estimated by
depending on emprical data which were simulated with different
values for the parameter of conditional probability distribution.
The best estimator for the power parameter was found in two
specified models by the maximum liklihood and Draper & Smith
methods. These estimators gave the best fit to the suggested
model and best estimator to the conditional expectations of
conditional probability distribution and it was concluded that the
suggested method was better than the ordinary method.
The increments in the probability of success (p) had a great effect
on the best fitted model also the estimated conditional
expectation of conditional binomial distribution was affected.
This result was clear because of decreasing the coefficient of
determination (R2) in Draper & Smith and the mean square of
residuals in maximum liklihood method with increase in (p)

This research boils down to find new ratio estimators instead of
estimators of (Kadilar and Cingi ;2004) by replacing the regression
parameter of the final estimators, which is estimated by ordinary least
squares, with new parameter estimated by bootstrapping regression
under specified conditions which have more accuracy than the first
estimators. The mean square error (MSE) was used to check the
accuracy of new estimators, Then we fiend Relative Efficiencies for
all proposed estimators. This work was supported with numerical
examples and simulations

The idea of this research is to find construction Weighted
Robust for the estimate parameters of the linear regression model
by blend Robust M- approach and Weighted Least Squares
method to handle the influence of Outlier Values, which don't
harmonies with the system of data set, in order to get new
estimators, The mean square error criterion is used to check the
efficiency of the new method with OLS , WLS and Robust M-
estimators