Abstract
This paper introduces the new novel four-parameter Weibull distribution named as the Marshall–Olkin alpha power Weibull (MOAPW) distribution. Some statistical properties of the distribution are examined. Based on Type-I censored and Type-II censored samples, maximum likelihood estimation (MLE), maximum product spacing (MPS), and Bayesian estimation for the MOAPW distribution parameters are discussed. Numerical analysis using real data sets and Monte Carlo simulation are accomplished to compare various estimation methods. This novel model’s supremacy upon some famous distributions is explained using two real data sets and it is shown that the MOAPW model can achieve better fits than other competitive distributions.
Highlights
In real-life phenomena, statistical distributions are widely used to describe these phenomena
Is paper’s aim is to introduce a new lifetime distribution defined as Marshall–Olkin alpha power Weibull (MOAPW) distribution, depending on the MOAP family
Parameter estimation for the MOAPW distribution using maximum likelihood estimation (MLE), maximum product spacing (MPS), and Bayesian estimation methods in the presence of Type-I and Type-II censoring is discussed in detail
Summary
In real-life phenomena, statistical distributions are widely used to describe these phenomena. Is paper’s aim is to introduce a new lifetime distribution defined as Marshall–Olkin alpha power Weibull (MOAPW) distribution, depending on the MOAP family. 3. Reliability Analysis e following equation defines the survival function of MOAPW distribution: θα − α 1− e−(x/β)λ. From equation (15), we can obtain the median (M) or the second quartile of MOAPW distribution when u 0.5 as follows:.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
