This paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model. The prior information is described as linear stochastic restrictions. We study the properties and the performances of these estimators compared to other common estimators using the mean squared error as a criterion for the goodness of fit. A numerical example and a simulation study are proposed to explain the performance of the estimators.

Synthetic anti-TB drugs are being used to treat tuberculosis (TB) as they are effective, however, they are accompanied by many side effects. The disease has remained largely uncured till date. The use of plant extracts or phytochemicals along with the anti-TB drugs is a very attractive strategy to make the treatment more effective as phytochemicals have no side-effects, are much less toxic than synthetic anti-TB drugs, are safe to use and most importantly, do not produce resistant strains as opposed to synthetic anti-TB drugs. Approximately 420,000 plant species have been identified globally and among them only a few have been explored for their therapeutic potential. Traditional medicine in different parts of the world has employed crud

Abstract

In this research we been estimated the survival function for data suffer from the disturbances and confusion of  Iraq Household Socio-Economic Survey: IHSES II 2012 , to data from a five-year age groups follow the distribution of the Generalized Gamma: GG. It had been used two methods for the purposes of estimating and fitting which is the way the Principle of Maximizing Entropy: POME, and method of booting to nonparametric smoothing function for Kernel, to overcome the mathematical problems plaguing integrals contained in this distribution in particular of the integration of the incomplete gamma function, along with the use of traditional way in which is the Maximum Likelihood: ML. Where the comparison on t

In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending

In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending on the mean square error criteria in where the estimation methods that were used are (Generalized Least Squares, M Robust, and Laplace), and for different sizes of samples (20, 40, 60, 80, 100, 120). The M robust method is demonstrated the best metho

Batch reactive distillation was studied in packed bed column. Esterification of methanol with acetic acid to produce methyl acetate and water with homogenous sulfuric acid as a catalyst was considered. This system was chosen because the reaction is reversible and the boiling point of reactant and products are different.
The reaction was carried out with and without distillation column and shows that the reactive distillation is more efficient from the conventional process (reactor and then separation). The conversion of acetic acid and concentration of methyl acetate increase by (30.43% and 75.14%) respectively at the best condition (reflux ratio 2, feed mole ratio 2 and batch time 90 minute).
The influence of various parameters, s

The logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables.                                                        The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.