Life Histories: Real and Synthetic

Abstract

Life history data are generally incomplete. Usually, they do not cover for each individual in the study the entire life span or the life segment of interest. If data are collected retrospectively, observation ends at interview date, and no information is available on events and experiences after the date. Data collected prospectively are incomplete because events and other experiences are recorded during a limited period of time only. To deal with data limitations, models are introduced. The model that is considered in this chapter describes life histories. The model is based on the premise that life histories are realisations of a continuous-time Markov process. A Markov process is a stochastic process that describes a system with multiple states and transitions between the states. The time at which a transition occurs is random but the distribution of the time to transition is known. In the continuous-time Markov process, the transition time has an exponential distribution. The rate of transition out of the current state (exit rate) is the parameter of the exponential distribution. It depends on the current state only and is independent of the history of the stochastic process. In a system with multiple states, an individual who leaves the current state may enter one of several states. In competing risks models, states in the state space are viewed as competing destinations and transition rates are destination-specific. The Markov process is a first-order process: the destination state depends on the current state only and is independent of states occupied previously.