Statistical inference based on left truncated and interval censored data from log-location-scale family of distributions

摘要

Here, left truncated and interval censored data are analyzed by assuming that the underlying lifetime distribution belongs to log-location-scale family. In particular, lognormal and Weibull models are considered. Steps of stochastic expectation maximization (St-EM) algorithm are developed for the estimation of model parameters. MLEs are also obtained using Newton-Raphson method. Asymptotic confidence intervals for parameters are constructed using missing information principle, and parametric bootstrap approach. Through a simulation study, performances of proposed inferential methods are assessed. St-EM algorithm for point estimation and parametric bootstrap approach for constructing confidence intervals are recommended under this setup. Two datasets are analyzed for illustrative purpose. A prediction problem is also discussed.