Abstract
This work considers point and interval estimation based on data from a life test under progressive type-I interval censoring with random removal. The asymptotic properties of the maximum likelihood estimators (MLEs) are established under appropriate regularity conditions. Asymptotic confidence intervals and $$\beta $$-content $$\gamma $$-level tolerance interval are obtained by using the asymptotic normality of MLEs. A simulation study is undertaken to assess the performance of the MLEs, confidence intervals and tolerance interval. Lastly, the minimum sample size required to achieve a desired $$\beta $$-content $$\gamma $$-level tolerance interval is determined.
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