This paper deals with, Bayesian estimation of the parameters of Gamma distribution under Generalized Weighted loss function, based on Gamma and Exponential priors for the shape and scale parameters, respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation. Based on Monte Carlo simulation method, those estimators are compared in terms of the mean squared errors (MSE’s).

 The ligand [Potassium (E)-(4-(((2-((1-(3-aminophenyl) ethylidene) amino)-4-oxo-1,4dihydropteridin-6-yl) methyl) amino)benzoyl)-L-glutamate] was prepared from the condensation reaction of folic acid with (3-aminoacetophenone) through Schiff reaction to give a new Schiff base ligand [H2L]. The ligand [H2L] was characterized by elemental analysis CHN, atomic absorption (A.A), (FT-I.R.), (U.V.-Vis), TLC, E.S. mass (for spectroscopes), molar conductance, and melting point. The new Schiff base ligand [H2L], reacts with Mn(II), Co(II), Ni(II), Cu(II), Cr(III) and Cd(II) metal ions and (2-aminophenol), (metal : derivative ligand : 2-aminophenol) to give a series of  new mixed complexes in  the general formula:- K3[M2(HL)(HA)2], (

       Synthesis and preliminary biological evaluation of imidazo (2, 1-b) Thiazole derivatives is reported. Under Mannich conditions, a series of new imidazo (2, 1-b) Thiazole derivatives were synthesized. Starting from the reaction of 2- amino thiazole with 4- bromo phenyl bromide to produce 5-(4-bromo phenyl) imidazo (2, 1-b) thiazoles, following by introduce the substituted aminomethyl at position 6-by reacting with different aromatic amines under Mannich conditions to afford 6-secondary amine-5-(4-bromo phenyl) imidazo (2,1-b) thiazole in high yields.

FT-IR, 1H NMR, and 13C NMR techniques were used to characterize the synthesized derivatives. In addition, all compounds were tested for their antioxidant activity, and thr

       D-mannose sugar was used to prepare [benzoic acid 6-formyl-2,2-dimethyl-tetrahydrofuro[3,4-d][1,3]dioxol-4-yl ester] (compound A). The condensation reaction of folic acid with (compound A) resulted in the formation of new ligand [L]. These compounds were characterized by elemental analysis CHN, atomic absorption A.A, (FT-I.R.), (U.V.-Vis), TLC, E.S. mass (for electrospray), molar conductance, and melting point. The new tetradentate ligand [L], reacted with two moles of some selected metal ions and two moles of (2-aminophenol), (metal : ligand : 2-aminophenol) at reflux in water medium to give a series of  new complexes of the general formula K2[M2(L)(HA)2]  where M= Co(II), Ni(II), Cu(II) an

In this publication, several six coordinate Co(III)-complexes are reported. The reaction of 2,3-butanedione monoxime with ethylenediamine or o-phenylenediamine in mole ratios of 2:1 gave the tetradentate imine-oxime ligands diaminoethane-N,N`-bis(2-butylidine-3-onedioxime) H2L1 and o-phenylenediamine-N,N`-bis(2-butylidine-3-onedioxime), respectively. The reaction of H2L1 and H2L2 with Co(NO3)2, and the amino acid co-ligands (glycine or serine) resulted in the formation of the required complexes. Upon complex formation, the ligands behave as a neutral tetradantate species, while the amino acid co-ligand acts as a monobasic species. The mode of bonding and overall geometry of the complexes were determined through physico-chemical and spectro

In this article, it is interesting to estimate and derive the three parameters which contain two scales parameters and one shape parameter of a new mixture distribution for the singly type one censored data which is the branch of right censored sample. Then to define some special mathematical and statistical properties for this new mixture distribution which is considered one of the continuous distributions characterized by its flexibility. Next,  using maximum likelihood estimator method for singly type one censored data based on the Newton-Raphson matrix procedure to find and estimate values of these three parameter by utilizing the real data taken from the National Center for Research and Treatment of Hematology/University of Mus

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function i

In this paper, some estimators for the reliability function R(t) of Basic Gompertz (BG) distribution have been obtained, such as Maximum likelihood estimator, and Bayesian estimators under General Entropy loss function by assuming non-informative prior by using Jefferys prior and informative prior represented by Gamma and inverted Levy priors. Monte-Carlo simulation is conducted to compare the performance of all estimates of the R(t), based on integrated mean squared.

      This paper deals with estimation of the reliability system in the stress- strength model of the shape parameter for the power distribution. The proposed approach has been including different estimations methods such as Maximum likelihood method, Shrinkage estimation methods, least square method and Moment method. Comparisons process had been carried out between the various employed estimation methods with using the mean square error criteria via Matlab software package.

In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).