# Use The moment method to Estimate the Reliability Function Of The Data Of Truncated Skew Normal Distribution

• Hatem Kareem Abbas
• Ahmed Dheyab Ahmed

### Abstract

The Estimation Of The Reliability Function Depends On The Accuracy Of The Data Used To Estimate The Parameters Of The Probability distribution, and Because Some Data Suffer from a Skew in their Data to Estimate the Parameters and Calculate the Reliability Function in light of the Presence of Some Skew in the Data, there must be a Distribution that has flexibility in dealing with that Data. As in the data of Diyala Company for Electrical Industries, as it was observed that there was a positive twisting in the data collected from the Power and Machinery Department, which required distribution that deals with those data and searches for methods that accommodate this problem and lead to accurate estimates of the reliability function, The Research Aims to Use The Method Of  Moment To Estimate The Reliability Function for Truncated skew-normal Distribution, As This Distribution Represents a Parameterized Distribution That is Characterized By flexibility in dealing with data that is Distributed Normally and Shows some Skewness. From the values ​​defined in the sample space, this means that a cut (Truncated) will be made from the left side in the Skew Normal Distribution and a new Distribution is Derived from the original Skew Distribution that achieves the characteristics of the Skew normal distribution function. Also, real data representing the operating times of three machines until the failure occurred were collected from The Capacity Department of Diyala Company for Electrical Industries, where the results showed that the machines under study have a good reliability index and that the machines can be relied upon at a high rate if they continue to work under the same current working conditions.

Published
2020-12-01
How to Cite
Abbas, H. and Ahmed, A. (2020) “Use The moment method to Estimate the Reliability Function Of The Data Of Truncated Skew Normal Distribution”, Journal of Economics and Administrative Sciences, 26(124), pp. 481-492. doi: 10.33095/jeas.v26i124.2053.
Issue
Section
Statistical Researches