IRAOI JOURNAL OF STATISTICAL SCIENCES

Recently, there is a great interest in the methods of Monte
Carlo used for the treatment of different technical and scientific
issues. This research deals with using the Monte Carlo methods
in numerical integration by making a general comparison
between the classical methods of integration and the Monte Carlo
methods. The research also applies the statistical sampling
method to compute the approximated values of a number of
numerical integrations. By doing so, we conclude that the Monte
Carlo methods are efficient in treating such important issues.

An expected cost model of a process whose mean is con-
trolled by an X chart is developed.
Tow- stage numerical procedure is used to calculate the
sample size, the number of units produced between sample, and
the control limits of optimal control charts.

This paper tackles the estimation of parameters of linear
mixed random effect one–classification model by Bayesian
technique which includes Gibbs sampling. Gibbs sampling is a
special case of Monte Carlo Method which uses Markov Chain
and so called MCMC (Markov Chain Monte Carlo).
This MCMC method depends on partition of difficult and
compound models into simple ones which can be manipulated
and easily analyzed, specially for the posterior distribution which
are not easy to find their final formulae.
In this research the mixed random effect linear one–classification
model is proposed on a population of 15 treatments including 15
types of cotton plant. A random sample of 5 types is taken and
using the analysis of variance method to test the hypothesis that
all the 15 types have equal effect and the estimation of the
parameters is obtained Gibbs sampling is also used in order to
estimate the parameters and then testing the hypothesis of equal
effects of treatments. The results obtained in both ANOVA and
Gibbs sampling are nearly the same and encouraging. All
algorithms are programmed in this research using WinBUGS
program.

Both inverse Gaussian and lognormal distributions have
been used among many well-known failure time distributions
with positively skewed data. The problem of selecting between
them is considered. The logarithm of maximum likelihood ratio
has been used as a test for discriminating between these two
distributions. The test has been carried out on nine different real
data sets and three simulated data sets.

n this paper , a revised model for radial basis function has been
done with application of sunspot time series. It has been arrived
at a revised model for nonlinear time series. The probability
characteristics have been explained for such series with chaotic
analysis of the simulated data from revised model ,which
indicates that the revised model is chaotic and bayesian
information criterion ( BIC) Schwarz information criterion
(SIC)and the error are better than radial basis function

In this paper we present new conjugate gradient method for
computing the minimum value of differentiable real valued
function in n variables ,this method derived from Nazareth
theorem , which uses the equivalence of CG and Qusi–Newton
methods on quadratic function also the descent property and
Conjucy conditions are proved and compared with some well
know CG method showing considerable improvement

The Assignment problem is really considered really very
important, which assigns a set of n distinct jobs
),.....,,( 321 nJJJJ to n machines ),.....,,( 321 nMMMM such
that, the total cost ∑ )( ijC is minimum. The problem is solved
by many methods, one of them is the Hungarian's method. This
research deals with graphs by proposing a new method which
deals with complete bipartite graph using Kruskal's method. We
compare the results for different problems which give optimal
solution in both methods, but the new method gives a high degree
of success and its easy to use.

Four types of algorithms are suggested in this paper to
smooth images; three of them work in the special domain and the
fourth works in the frequency domain. The first genetic
algorithm uses smoothing filters which are called the median,
mean or minimum as an objective function for the genetic algorithm each time. A style has been proposed to enhance the
mean filter. The second genetic algorithm uses processes of
morphology to perform smoothing operation, while the third and
fourth ones utilize Gaussian filter as an objective function, but
they differ in that the third genetic algorithm works in the special
domain and the fourth in the frequency one

In this paper, we study methods to obtain the constant
smoothing of exponential smoothing model and suggest another
method, to obtain constant smoothing of dependent (two
estimation New and old Information) of dependent adaptive
filtering ,and applied for time series to National Income overall
of the Egypt for (1965-2002) and unsure of use this model to
use the measure of (χ2), simulation have been done depending on
the parameter of the original series with different numbers.

This research deals with a state space models and principle
components analysis approach to estimate the delay time in linear
stochastic dynamical systems. The delay time has a statistical
importance to describe the suitable identification. The results are
compared with the proposed approach from the simulation
experiments where the Principle Component approach gives a
high degree of success to estimate the delay time with state space
where there isn’t any failure state compared with original data.

This paper used the technique of Network Sampling for
estimation the diffusion of a hevidetary disease of the middle east
one mail (Thalassemia) in Tala'fur city, and a comparison of the
efficiency of this technique with some ordinary techniques was
made in estimation , and it has been concluded that this technique
had been characterized with less variance and giving it a near
actual estimators with the remaining known ordinary techniques
in which the most important one is the technique of proportion
sampling .

In this paper calculation of averages of measurements of
newly born baby are obtained; namely, the weight , height ,size
circumference of head, chest , and arm; through which the health
of the baby may be decided using Bayes estimates depending on
the standard information of the previous distribution and
compared it with the traditional estimate depending on sample
information only.

One of the Problems of image processing is that there is
plenty of hidden information among the gray levels of these
images. In this research the histogram technique is widely used
for image enhancement to extract that information. Through the
applications applied to different types of images better results
were obtained compared with the original ones which were
processed via the proposed algorithm

In this research, the genetic algorithm (GA) has been carried
out. This application is considered to be a manufacturing
treatment to find the value which maximizes the likelihood
function . An algorithm has been proposed to find the values
which maximize. The likelihood function for normal distribution.
The application of this algorithm enables us to find out several
solutions, including the value which is responsible for the
maximization of the likelihood function and the number of the
latter is equal to the times for generating the algorithm