In the current study, the researchers have been obtained Bayes estimators for the shape and scale parameters of Gamma distribution under the precautionary loss function, assuming the priors, represented by 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 depending on the mean squared errors (MSE’s). The results show that, the performance of Bayes estimator under precautionary loss function with Gamma and Exponential priors is better than other estimates in all cases.

In this paper, Bayes estimators for the shape and scale parameters of Weibull distribution have been obtained using the generalized weighted loss function, based on Exponential priors. Lindley’s approximation has been used effectively in Bayesian estimation. Based on theMonte Carlo simulation method, those estimators are compared depending on the mean squared errors (MSE’s).

In this research, the focus was placed on estimating the parameters of the Hypoexponential distribution function using the maximum likelihood method and genetic algorithm. More than one standard, including MSE, has been adopted for comparison by Using the simulation method

A group of acceptance sampling to testing the products was designed when the life time of an item follows a log-logistics distribution. The minimum number of groups (k) required for a given group size and acceptance number is determined when various values of Consumer’s Risk and test termination time are specified. All the results about these sampling plan and probability of acceptance were explained with tables.

In this paper, two parameters for the Exponential distribution were estimated using the
Bayesian estimation method under three different loss functions: the Squared error loss function,
the Precautionary loss function, and the Entropy loss function. The Exponential distribution prior
and Gamma distribution have been assumed as the priors of the scale γ and location δ parameters
respectively. In Bayesian estimation, Maximum likelihood estimators have been used as the initial
estimators, and the Tierney-Kadane approximation has been used effectively. Based on the MonteCarlo
simulation method, those estimators were compared depending on the mean squared errors (MSEs).The results showed that the Bayesian esti

This paper interest to estimation the unknown parameters for generalized Rayleigh distribution model based on censored samples of singly type one . In this paper the probability density function for generalized Rayleigh is defined with its properties . The maximum likelihood estimator method is used to derive the point estimation for all unknown parameters based on iterative method , as Newton – Raphson method , then derive confidence interval estimation which based on Fisher information matrix . Finally , testing whether the current model ( GRD ) fits to a set of real data , then compute the survival function and hazard function for this real data.

The goal of this study was to investigate the protein peroxidation role by measuring serum levels of advanced oxidation protein products (AOPP) in type 2 diabetic patients with or without retinopathy and comparing them to controls to see if circulating AOPP levels can be used as a detection biomarker for DR. And see which of the two widely used antidiabetic treatment groups had the most impact on this oxidative stress marker. The groups were divided into two subgroups: 1) 70 type 2 diabetic patients (36 male, 34 female), 35 with diabetic retinopathy (DR) and 35 with no evidence of DR, and 2) non-diabetic controls (11 male, 9 female) were chosen from Ibn AL-Haitham Hospital for Ophthalmology and a Specialized Center for Endocrinology and Dia

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).

Background: Family history of type 2 diabetes is a major risk factor for type 2 diabetes. Type 2 diabetes is characterized by progressive β-cell dysfunction; many investigators have used C peptide levels as a biomarker of β-cell function.

Objective: the current study was design to investigate the impact of family history on biochemical characteristics (c-peptide, HbA1c, lipid profile, insulin secretion, insulin sensitivity and insulin resistance.

Subjects and me

Changes in mechanical properties of material as a result of service in different conditions can be provided by mechanical testing to assist the estimation of current internal situation of these materials, or the degree of deterioration may exist in furnaces serviced at high temperature and exceed their design life. Because of the rarity works on austenitic stainless steel material type AISI 321H, in this work, ultimate tensile strength, yield strength, elongation, hardness, and absorbed energy by impact are evaluated based on experimental data obtained from mechanical testing. Samples of tubes are extracted from furnace belong to hydrotreaterunit, also samples from un-used tube material are used to make comparisons between these properti