Semiparametric estimation of censored transformation models

Many widely used models, including proportional hazards models with unobserved heterogeneity, can be written in the form Λ(Y) = min [β′X + U, C], where Λ is an unknown increasing function, the error term U has unknown distribution function Ψ and is independent of X, C is a random censoring threshold and U and C are conditionally independent given X. This paper develops new n^1/2-consistent and asymptotically normal semiparametric estimators of Λ and Ψ which are easier to use than previous estimators. Moreover, Monte Carlo results suggest that the mean integrated squared error of predictions based on the new estimators is lower than for previous estimators.