A version of the discrete proportional hazards model is developed for psychometrical applications. In such applications, a primary covariate that influences failure times is a latent variable representing a psychological construct. The Metropolis-Hastings algorithm is studied as a method for performing marginal likelihood inference on the item parameters. The model is illustrated with a real data example that relates the age at which teenagers first experience various substances to the latent ability to avoid the onset of such behaviors.
- Latent variable
- Metropolis-hastings algorithm
- Survival analysis
ASJC Scopus subject areas
- Mathematics (miscellaneous)
- Psychology (miscellaneous)
- Social Sciences (miscellaneous)