Estimation of the Bivariate Kumaraswamy Lifetime Distribution under Progressive Type-I Censoring

Analyzing time to event data arises in a number of fields such as Biology and Engineering. A common feature this is that, the exact failure for all units may not be observable. Accordingly, several types censoring were presented. Progressive allows randomly removed before terminal point experiment. Marshall-Olkin bivariate lifetime distribution was first introduced 1967 using exponential distribution. Recently, Kumaraswamy derived. This paper derives likelihood function under progressive type-I family general applies it on Maximum estimators model parameters Simulation study real set are presented illustrate proposed procedure. Absolute bias, mean square error, asymptotic confidence intervals, width coverage probability obtained. results indicate that error smaller narrower more precise when removals gets smaller. Also, increasing experiment reducing width.