elham ismail

Stratified random sampling is an effective sampling technique for estimating the population characteristics. The determination of strata boundaries and the allocation of sample size to the strata are two of the most critical factors in maximizing the precision of the estimates. Most surveys are conducted in an environment of severe budget constraints and a specific time is required to finish the survey. So cost and time are two important objectives that are taken under consideration in most surveys. The study suggested Mathematical goal programming model for determining optimum stratum boundaries for an exponential study variable under multiple objectives model when cost and time are under consideration. Compared to other techniques, Goal programming has many advantages in resources planning. Determining the required resources to satisfy the desired goals and the effectiveness of the available resources as well as providing best solutions under different amounts of resources are exa...

Quantile regression is a statistical technique intended to estimate, and conduct inference about the conditional quantile functions. Just as the classical linear regression methods estimate models for conditional mean function, quantile regression offers a mechanism for estimating models for conditional median function, and the full range of other conditional quantile functions. In this paper describe, compare, and apply the two quantile regression (L1-Lasso, L2-Lasso) suggested approaches. The two quantile regression suggested approaches used to select the best subset of variables and estimate the parameters of the quantile regression equation when small sample sizes are used. The aim of this study is to study the behavior of L1Lasso and L2 -Lasso quantile regression method when small sample sizes are generated. Simulations show that the proposed approaches are very competitive in terms of variable selection, estimation accuracy and efficient when small sample sizes are used. All r...

Data Envelopment Analysis is a powerful technique for measuring the relative efficiency of organizational units with multiple inputs and outputs. This approach was introduced by Charnes, Cooper and Rhodes in 1978, and is gradually becoming a useful management tool. In addition to the efficiency score, Data Envelopment Analysis (DEA) indicates targets for inefficient units. The purpose of this paper is to investigate the performance of fifteen Kingdom of Saudi Arabia universities for the academic year 2013. The study evaluates the technical efficiency of individual Saudi Arabia universities using the nonparametric frontier methodology, the Data Envelopment Analysis (DEA). To investigate the determinants of efficiency, the study use the Tobit regression. This analysis aims to explain the variation in calculated efficiencies to a set of explanatory variables, i.e. total number of students enrolled in undergraduate, graduates, number of academic staff, number of non-academic staff, and ...

Queuing models applications are centered on the question of finding the ideal level of services, waiting times and queue lengths. The aim of this study is to measure the cost for three models and compare the cost for the three single channel waiting line models instead of finding the ideal level of services, waiting times and queue lengths which calculated in many studies.  Each model depends on two important parameters arrival rate (λ) and service rate (μ) which followed different distributions.  The cost for the three single channel waiting line models is calculated when arrival rate (λ) is followed Poisson distribution and service rate (μ) is followed different distributions. The objective for the waiting line models is to minimize total expected costs by minimize the sum of service costs and waiting costs. Therefore, the study concerned with changing the distribution of the service rate (μ) and examining its impact on cost. This choice was made to emphasize the basic idea of the...

The study is concerned with the transforming theoretical Mathematical models into applied Mathematical programming models that are easy to handle and use. These Mathematical programming models can be applied and used in statistical inference, which used in many applied fields, for example, quality control and its application. The aim of this paper is to suggest two mathematical programming models for hypotheses tests, which make a balance between the high power (1-β), and the probability of a type I error, significance (), of the test. The paper introduces a simulation study to evaluate the performance of the two suggested mathematical programming models for tests hypotheses. The two suggested mathematical programming models solved with different sample sizes and different level of significance. The suggested models calculate the critical values which determine the rejection region exactly and the results are easy to interpret clearly. Then the conclusion for the suggested mathemati...