Reliability of stress-strength model for exponentiated Teissier distribution based on lower record values

The exponentiated Teissier distribution was recently introduced which has more flexible hazard rate functions than several commonly-used distributions in the lifetime literature. In this endeavour, we study the reliability of stress-strength model based on lower record values when the distribution of the strength and stress are both exponentiated Teissier. The maximum likelihood and Bayes estimators of the reliability of the model are determined when the common scale parameter is unknown or known. In addition, the uniformly minimum variance unbiased estimate is computed when the common scale parameter is known. The asymptotic confidence interval and the highest probability density credible interval are constructed. Furthermore, another asymptotic confidence interval is created based on arcsin transformation. The mean squared error criterion is considered to evaluate the point estimates and average length and coverage probability criteria are applied to evaluate the interval estimates. One real example is shown on the side of the suggested methods as well as for the flexibility of the exponentiated Teissier distribution.