@article{MAKHILLJEAS201914417435,
    title = {Comparing Different Estimators of Reliability Function for Stress-Strength
Models with Applications},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {4},
    pages = {1138-1141},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.1138.1141},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.1138.1141},
    author = {Inaam Rikan},
    keywords = {Stress-strength model,least square method,maximum likelihood method,reliability function,sets,MSE},
    abstract = {One of the most practical application of reliability as a function of time is a well stress-strength model
  were this model have several applications like Physics, engineering and components, so here, we introduce the
  Stress-Strength (S-S) reliability model for system contains one component denoted by [R = p(y<x)] where (y)
  is stress random variable and (x) is strength random variable were: </p>

<table width="100%" border="0">
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    <td><div align="center"><img src="http://medwelljournals.com/images/img1-2k19-1138-1141.gif" width="191" height="39" /></div></td>
  </tr>
</table>
<p>The studied model introduced represents reliability function for stress-strength model, assuming the
components of stress and strength are independent and identically distributed as Exponentiated Weibull
Distribution (EWD). The model of S-S derived and the reliability of it also found. Then estimating by maximum
likelihood and least square methods. The comparison done through simulation using different sets of sample
size (n, m) also different sets of initial values of (&beta;<sub>1</sub>, &beta;<sub>2</sub>, &theta;), all the results of comparison explained by tables.</p>}
    }