Comparative between MLE and Pattern Search for Reliability Estimation of the Teissier Distribution”
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Abstract
The Teissier distribution is a modern distribution that is suitable for data used in machine learning and in production factors such as cotton, textile, furniture factories, and the like. It is also suitable for data used in medical fields, example that important devices associated with major operations like brain or heart surgeries, or cancer patients whose data requires monitoring.
In this research, the maximum likelihood method and the pattern search algorithm were used as methods to estimate the reliability function of this distribution. The results showed that the pattern search algorithm is suitable for medium and large samples, which confirms its superiority over the maximum likelihood estimation method at these sizes and for the default value β = 0.2. At the default value of β=0.1, the maximum likelihood estimation method outperformed the pattern search algorithm for all sample reservations.
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