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In this study, we used Bayesian method to estimate scale parameter for the normal distribution. By considering three different prior distributions such as the square root inverted gamma (SRIG) distribution and the non-informative prior distribution and the natural conjugate family of priors. The Bayesian estimation based on squared error loss function, and compared it with the classical estimation methods to estimate the scale parameter for the normal distribution, such as the maximum likelihood estimation and the moment estimation. Several cases from normal distribution for data generating, or different sample sizes (small, medium, and large). The results were obtained by using simulation technique, Programs written using MATLAB-R2008a program were used .Simulation results shown that bayes estimation when the prior distribution is (SRIG) distribution with (a=3, b=1) for , and with (a=b=3) for , and with (a=2, b=3) for , and with (a=1, b=3) for gives the smallest value of MSE and MAPE for all sample sizes.

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We produced a study in Estimation for Reliability of the Exponential distribution based on the Bayesian approach. These estimates are derived using Bayesian approaches. In the Bayesian approach, the parameter of the Exponential distribution is assumed to be random variable .we derived bayes estimators of reliability under four types when the prior distribution for the scale parameter of the Exponential distribution is: Inverse Chi-square distribution, Inverted Gamma distribution, improper distribution, Non-informative distribution. And estimators for Reliability is obtained using the well known squared error loss function and weighted squared errors loss function. We used simulation technique, to compare the resultant estimators in terms of their mean squared errors (MSE), mean weighted squared errors (MWSE).Several cases assumed for the parameter of the exponential distribution for data generating, of different samples sizes (small, medium, and large). The results were obtained by using simulation technique, Programs written using MATLAB-R2008a program were used. In general, Simulation results shown that the resultant estimators in terms of their mean squared errors (MSE) is better than the resultant estimators in terms of their mean weighted squared errors (MWSE).According to the our criteria is the best estimator that gives the smallest value of MSE or MWSE . For example bayes estimation is the best when the prior distribution for the scale parameter is improper and Non-informative distributions according to the smallest value of MSE comparative to the values of MWSE for all samples sizes at some of true value of t and .

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In this study, we used Bayesian method to estimate scale parameter for the Nakagami distribution. By considering several of double prior distributions for the scale parameter of the Nakagami distribution ,it means we have two different information about the prior distributions , such as inverted exponential- levy distribution and inverted exponential -gumbel type-II distribution and levy - gumbel type-II distribution and Inverted exponential -non- informative distribution and levy- Non- informative distribution and gumbel type-II - non- informative distribution.We derived the posterior distribution for the scale parameter of the Nakagami distribution according to each of double prior distributions. Also, we derived bayes estimators for the scale parameter of the Nakagami distribution based on squared error loss function. we used simulation technique to compare between these estimators. Several cases from Nakagami distribution for data generating, or different sample sizes (small, medium, and large),by programs written using MATLAB-R2017b program.Simulation results shown that the best estimation for the scale parameter for the Nakagami distribution according to the smallest value of MSE all sample sizes, when the double prior distribution is gumbel type-II- Non- Informative distribution with the parameters for and and .Also, when the double prior distribution is inverted exponential -gumbel type-II distribution with the parameters for .Also, when the double prior distribution is levy- non- informative distribution with the parameters for .Also, when the double prior distribution is inverted exponential- levy distribution with the parameters with .

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Statistical control charts are widely used in industry for process and measurement control . in this paper we study the use of markov chain approach in calculating the average run length (ARL) of cumulative sum (Cusum) control chart for defect the shifts in the mean of process , and exponentially weighted moving average (EWMA) control charts for defect the shifts for process mean and , the standard deviation . Also ,we used the EWMA charts based on the logarithm of the sample variance for monitoring a process standard deviation when the observations (products are selected from al_mamun factory ) are identically and independently distributed (iid) from normal distribution in continuous manufacturing . The case study approach used as the appropriate approach to reach the research objectives , all of the (Product toothpaste Anber , Product Hand Cream Balsam , Product Menem Eutur fabrics, and Bathroom Cleaner Rwnaq ) represent for the research sample , the statistical methods used are the Cusum and EWMA chats And the adoption of the (ARL) and (SDRL) obtained using the method of Markov chain.The researcher recommended that the application of the standard Average Run length of ARL to reach the production process and disciplined statistically reduce deviations in the production process for the target value .

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In this study, different estimators were used for estimating scale parameter for the Maxwell–Boltzmann distribution, such as maximum likelihood estimator, moment estimator and the Bayes estimator, in three types when the prior distribution for the scale parameter is (SRIG) distribution and ,the non-informative prior distribution and, the natural conjugate family of priors when the Bayesian estimation based on Squared Loss Function. Several cases from Maxwell–Boltzmann distribution for data generating , for different sample sizes (small, medium, and large).The results were obtained by using simulation technique, Programs written using MATLAB-R2008a program were used. Simulation results shown that bayes estimation when the prior distribution is (SRIG) distribution with (a=b=3) gives the smallest value of MSE and MAE for all (n).And bayes estimation when the prior distribution is the non-informative prior distribution with ( c=6) gives the smallest value of MSE and MAE for all (n).

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In this study, we derived the estimation for Reliability of the Exponential distribution based on the Bayesian approach. In the Bayesian approach, the parameter of the Exponential distribution is assumed to be random variable .We derived posterior distribution the parameter of the Exponential distribution under four types priors distributions for the scale parameter of the Exponential distribution is: Inverse Chi-square distribution, Inverted Gamma distribution, improper distribution, Non-informative distribution. And the estimators for Reliability is obtained using the two proposed loss function in this study which is based on the natural logarithm for Reliability function .We used simulation technique, to compare the resultant estimators in terms of their mean squared errors (MSE).Several cases assumed for the parameter of the exponential distribution for data generating of different samples sizes (small, medium, and large). The results were obtained by using simulation technique, Programs written using MATLAB-R2008a program were used. In general, we obtained a good estimations of reliability of the Exponential distribution under the second proposed loss function according to the smallest values of mean squared errors (MSE) for all samples sizes (n) comparative to the estimated values for MSE under the first proposed loss function

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AbstractIn this study, different estimators were used for estimating parameter of the exponential distribution, such as maximum likelihood estimator, moment estimator and the Bayes estimator, by assuming six types when the prior distribution for the scale parameter is: Levy distribution, Gumbel type-II distribution, Inverse Chi-square distribution, Inverted Gamma distribution, improper distribution, Non-informative distribution .Under squared and weighted squared error loss functions. We used simulation technique, to compare the performance for each estimator, several cases from Exponential distribution for data generating, for different samples sizes (small, medium, and large). Simulation results shown that The best method is the bayes estimation according to the smallest values of MSE & MWSE for all samples sizes (n) comparative to the estimated values by using Maximum likelihood estimation method (MLE) and Moment estimation method (ME). According to obtained results, we see that when the prior distribution for is Inverted Gamma distribution for some values of the parameters , given the best results according to the smallest values of MSE & MWSE comparative to the same values which obtained by using MLE& ME for the assuming true values by and for all samples sizes. When the prior distribution for is Improper distribution for some values of the parameters a & b, given the best results according to the smallest values of MSE & MWSE comparative to the same values which obtained by using MLE & ME for the assuming true values by and all samples sizes.

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In this paper, we used maximum likelihood method and the Bayesian method to estimate the shape parameter (θ), and reliability function (R(t)) of the Kumaraswamy distribution with two parameters  θ (under assuming the exponential distribution, Chi-squared distribution and Erlang-2 type distribution as prior distributions), in addition to that we used method of moments for estimating the parameters of the prior distributions. Bayes estimators derived under the squared error loss function. We conduct simulation study, to compare the performance for each estimator, several values of the shape parameter (θ) from Kumaraswamy distribution for data-generating, for different samples sizes (small, medium, and large). Simulation results have shown that the Best method is the Bayes estimation according to the smallest values of mean square errors(MSE) for all samples sizes (n).

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