Is to obtain competitive advantage legitimate objective pursued by all organizations to achieve, because they live today in environments of rapid change and dynamic in order to meet the demands of the customer changing as well as intense competition between the organizations, which requires them to get the location of competitive markets in order to do this will remain to do the building and strengthening competitive advantage to be able to achieve, but that this feature is not easy and is not only through the identification and use of a successful strategy for a competitive standard and then manage it successfully. Hence the research problem of determining the sources of differentiation strategy and its impact on the dimensions of compe

This paper is interested in comparing the performance of the traditional methods to estimate parameter of exponential distribution (Maximum Likelihood Estimator, Uniformly Minimum Variance Unbiased Estimator) and the Bayes Estimator in the case of data to meet the requirement of exponential distribution and in the case away from the distribution due to the presence of outliers (contaminated values). Through the employment of simulation (Monte Carlo method) and the adoption of the mean square error (MSE) as criterion of statistical comparison between the performance of the three estimators for different sample sizes ranged between small, medium and large        (n=5,10,25,50,100) and different cases (wit

In this paper, we made comparison among different parametric ,nonparametric and semiparametric estimators for partial linear regression model users parametric represented by ols and nonparametric methods represented by cubic smoothing spline estimator and Nadaraya-Watson estimator, we study three nonparametric regression models and samples sizes  n=40,60,100,variances used σ²=0.5,1,1.5 the results  for the first model show that N.W estimator for partial linear regression model(PLM) is the best followed the cubic smoothing spline estimator for (PLM),and the results of the second and the third model show that the best estimator is C.S.S.followed by N.W estimator for (PLM) ,the

One of the most significant elements influencing weather, climate, and the environment is vegetation cover. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) over the years 2019–2022 were estimated based on four Landsat 8 TIRS’s images covering Duhok City. Using the radiative transfer model, the city's land surface temperature (LST) during the next four years was calculated. The aim of this study is to compute the temperature at the land's surface (LST) from the years 2019-2022 and understand the link, between LST, NDVI, and NDBI and the capability for mapping by LANDSAT-8 TIRS's. The findings revealed that the NDBI and the NDVI had the strongest correlation with the

In this study, we focused on the random coefficient estimation of the general regression and Swamy models of panel data. By using this type of data, the data give a better chance of obtaining a better method and better indicators. Entropy's methods have been used to estimate random coefficients for the general regression and Swamy of the panel data which were presented in two ways: the first represents the maximum dual Entropy and the second is general maximum Entropy in which a comparison between them have been done by using simulation to choose the optimal methods.

The results have been compared by using mean squares error and mean absolute percentage error to different cases in term of correlation valu

This research aims to study the methods of reduction of dimensions that overcome the problem curse of dimensionality when traditional methods fail to provide a good estimation of the parameters So this problem must be dealt with directly . Two methods were used to solve the problem of high dimensional data, The first method is the non-classical method Slice inverse regression ( SIR ) method and the proposed weight standard Sir (WSIR) method and principal components (PCA) which is the general method used in reducing dimensions,    (SIR ) and (PCA) is based on the work of linear combinations of a subset of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of linear

The current research aims to provide a conceptual and applied frame  on the subject of multi- level analysis in the research of business administration. The research tries to address some of the problems that befall the preparation of research and studies at the Arab level and local level, where the unity of theory and measurement and analysis, as well as clarify  the various types of conceptual constructs and give researchers the ability to  distinguish different models related to the level of analysis. On the other hand, this research  provides an example of 

The particle-hole state densities have been calculated for 232Th in
the case of incident neutron with  ,  1 Z Z T T T T and   2 Z T T .
The finite well depth, surface effect, isospin and Pauli correction are
considered in the calculation of the state densities and then the
transition rates. The isospin correction function ( ) iso f has been
examined for different exciton configurations and at different
excitation energies up to 100 MeV. The present results are indicated
that the included corrections have more affected on transition rates
behavior for        , , and    above 30MeV excitation energy

The question of estimation took a great interest in some engineering, statistical applications, various applied, human sciences, the methods provided by it helped to identify and accurately the many random processes.

In this paper, methods were used through which the reliability function, risk function, and estimation of the distribution parameters were used, and the methods are (Moment Method, Maximum Likelihood Method), where an experimental study was conducted using a simulation method for the purpose of comparing the methods to show which of these methods are competent in practical application This is based on the observations generated from the Rayleigh logarithmic distribution (RL) with sample sizes