Preferred Language
Articles
/
7xhhD5UBVTCNdQwCgySl
Estimation of the reliability function of the Rayleigh distribution using some robust and kernel methods
...Show More Authors
Abstract<p>The research presents the reliability. It is defined as the probability of accomplishing any part of the system within a specified time and under the same circumstances. On the theoretical side, the reliability, the reliability function, and the cumulative function of failure are studied within the one-parameter Raleigh distribution. This research aims to discover many factors that are missed the reliability evaluation which causes constant interruptions of the machines in addition to the problems of data. The problem of the research is that there are many methods for estimating the reliability function but no one has suitable qualifications for most of these methods in the data such as the presence of anomalous values or extreme values or the appropriate distribution of these data is unknown. Therefore, the data need methods through which can be dealt with this problem. Two of the estimation methods have been used: the robust (estimator M) method and the nonparametric Kernel method. These estimation methods are derived to arrive at the formulas of their capabilities. A comparison of these estimations is made using the simulation method as it is implemented. Simulation experiments using different sample sizes and each experiment is repeated (1000) times to achieve the objective. The results are compared by using one of the most important statistical measures which is the mean of error squares (MSE). The best estimation method has been reached is the robust (M estimator) method. It has been shown that the estimation of the reliability function gradually decreases with time, and this is identical to the properties of this function.</p>
Scopus Crossref
View Publication
Publication Date
Wed Sep 01 2010
Journal Name
Journal Of Economics And Administrative Sciences
Using simulation to estimate parameters and reliability function for extreme value distribution
...Show More Authors

   This study includes Estimating scale parameter, location parameter  and reliability function  for Extreme Value (EXV) distribution by two methods, namely: -
- Maximum Likelihood Method (MLE).
- Probability Weighted Moments Method (PWM).

 Used simulations to generate the required samples to estimate the parameters and reliability function of different sizes(n=10,25,50,100) , and give real values for the parameters are and , replicate the simulation experiments (RP=1000)

... Show More
View Publication Preview PDF
Crossref
Publication Date
Mon Apr 20 2020
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Bayesian Inference for the Parameter and Reliability Function of Basic Gompertz Distribution under Precautionary loss Function
...Show More Authors

     In this paper, some estimators for the unknown shape parameter and reliability function of Basic Gompertz distribution have been obtained, such as Maximum likelihood estimator and Bayesian estimators under Precautionary loss function using Gamma prior and Jefferys prior. Monte-Carlo simulation is conducted to compare mean squared errors (MSE) for all these estimators for the shape parameter and integrated mean squared error (IMSE's) for comparing the performance of the Reliability estimators. Finally, the discussion is provided to illustrate the results that summarized in tables.

View Publication Preview PDF
Crossref
Publication Date
Tue Dec 31 2019
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Different Estimators for the shape Parameter and the Reliability function of Kumaraswamy Distribution
...Show More Authors

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 l , 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

... Show More
View Publication Preview PDF
Crossref
Publication Date
Mon Apr 20 2020
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Some Aspects of Weighted Rayleigh Distribution
...Show More Authors

In this paper, we proposed a new class of weighted Rayleigh distribution based on two parameters, scale and shape parameters which are introduced in Rayleigh distribution. The main properties of this class are investigated and derived.

View Publication Preview PDF
Crossref
Publication Date
Sun Nov 18 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
The Comparison Between Standard Bayes Estimators of the Reliability Function of Exponential Distribution
...Show More Authors

   In this paper, a Monte Carlo Simulation technique is used to compare the performance of the standard Bayes estimators of the reliability function of the one parameter exponential distribution .Three types of loss functions are adopted, namely, squared error  loss function (SELF) ,Precautionary error loss function (PELF) andlinear exponential error  loss function(LINEX) with informative and non- informative prior .The criterion integrated mean square error (IMSE) is employed to assess the performance of such estimators

View Publication Preview PDF
Crossref (1)
Crossref
Publication Date
Fri May 01 2020
Journal Name
Journal Of Physics: Conference Series
Bayesian Inference for Reliability Function of Gompertz Distribution
...Show More Authors
Abstract<p>In this paper, some Bayes estimators of the reliability function of Gompertz distribution have been derived based on generalized weighted loss function. In order to get a best understanding of the behaviour of Bayesian estimators, a non-informative prior as well as an informative prior represented by exponential distribution is considered. Monte-Carlo simulation have been employed to compare the performance of different estimates for the reliability function of Gompertz distribution based on Integrated mean squared errors. It was found that Bayes estimators with exponential prior information under the generalized weighted loss function were generally better than the estimators based o</p> ... Show More
View Publication Preview PDF
Scopus (1)
Crossref (1)
Scopus Crossref
Publication Date
Tue Oct 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Between Shrinkage &Maximum likelihood Method For Estimation Parameters &Reliability Function With 3- Parameter Weibull Distribution By Using Simulation
...Show More Authors

The 3-parameter Weibull distribution is used as a model for failure since this distribution is proper when the failure rate somewhat high in starting operation and these rates will be decreased with increasing time .

In practical side a comparison was made between (Shrinkage and Maximum likelihood) Estimators for parameter and reliability function using simulation , we conclude that the Shrinkage estimators for parameters are better than maximum likelihood estimators but the maximum likelihood estimator for reliability function is the better using statistical measures (MAPE)and (MSE) and for different sample sizes.

Note:- ns : small sample ; nm=median sample

... Show More
View Publication Preview PDF
Crossref
Publication Date
Tue Jan 01 2019
Journal Name
Baghdad Science Journal
Hazard Rate Estimation Using Varying Kernel Function for Censored Data Type I Article Sidebar
...Show More Authors

n this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used: local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the types of the kernel boundary func

... Show More
View Publication
Scopus (3)
Scopus Clarivate Crossref
Publication Date
Mon Jul 01 2019
Journal Name
Iop Conference Series: Materials Science And Engineering
On Estimation of the Stress – Strength Reliability Based on Lomax Distribution
...Show More Authors
Abstract<p>The present paper concerns with the problem of estimating the reliability system in the stress – strength model under the consideration non identical and independent of stress and strength and follows Lomax Distribution. Various shrinkage estimation methods were employed in this context depend on Maximum likelihood, Moment Method and shrinkage weight factors based on Monte Carlo Simulation. Comparisons among the suggested estimation methods have been made using the mean absolute percentage error criteria depend on MATLAB program.</p>
View Publication
Scopus (5)
Crossref (2)
Scopus Crossref
Publication Date
Mon Aug 01 2022
Journal Name
International Journal Of Nonlinear Analysis And Applications
comparing three estimators of fuzzy reliability for one scale parameter rayleigh distribution
...Show More Authors

Statistical methods and statistical decisions making were used to arrange and analyze the primary data to get norms which are used with Geographic Information Systems (GIS) and spatial analysis programs to identify the animals production and poultry units in strategic nutrition channels, also the priorities of food insecurity through the local production and import when there is no capacity for production. The poultry production is one of the most important commodities that satisfy human body protein requirements, also the most important criteria to measure the development and prosperity of nations. The poultry fields of Babylon Governorate are located in Abi Ghareg and Al_Kifil centers according to many criteria or factors such as the popu

... Show More
Scopus (1)
Scopus