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Inference in mixed proportional hazard models with K random effects


A general formulation of mixed proportional hazard models with K random effects is provided. It enables to account for a population stratified at K different levels. I then show how to approximate the partial maximum likelihood estimator using an EM algorithm. In a Monte Carlo study, the behavior of the estimator is assessed and I provide an application to the ratification of ILO conventions. Compared to other procedures, the results indicate an important decrease in computing time, as well as improved convergence and stability.

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Correspondence to Guillaume Horny.

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Horny, G. Inference in mixed proportional hazard models with K random effects. Stat Papers 50, 481–499 (2009).

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  • EM algorithm
  • Penalized likelihood
  • Partial likelihood
  • Frailties
  • Duration analysis