Most event history data that spans through a long period of time requires models that reflect sequence of non-homogeneous time windows within that interval. We present procedure for obtaining parametric and non-parametric maximum likelihood estimates for censored and truncated data within the time windows. The method is very useful especially when there are remarkable events at the change points for each time window. It can be widely applied in a variety of disciplines including disease history, social mobility and human resource planning.
