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A new two-parameter lifetime distribution with increasing failure rate is introduced. Various properties of the proposed distribution are discussed. The distribution parameters are estimated by the EM algorithm and the asymptotic variances and covariances of these estimators are obtained. The accuracy of maximum likelihood estimation of variances and covariances are studies by simulation. Some experimental results based on real data sets illustrate the results.

This paper provides Bayesian and classical inference of Stress–Strength reliability parameter, [Formula: see text], where both [Formula: see text] and [Formula: see text] are independently distributed as 3-parameter generalized linear failure rate (GLFR) random variables with different parameters. Due to importance of stress–strength models in various fields of engineering, we here address the maximum likelihood estimator (MLE) of [Formula: see text] and the corresponding interval estimate using some efficient numerical methods. The Bayes estimates of [Formula: see text] are derived, considering squared error loss functions. Because the Bayes estimates could not be expressed in closed forms, we employ a Markov Chain Monte Carlo procedure to calculate approximate Bayes estimates. To evaluate the performances of different estimators, extensive simulations are implemented and also real datasets are analyzed.