Accelerated failure time models with log-concave errors

Ruixuan Liu and Zhengfei Yu

The Econometrics Journal, 2020, vol. 23, issue 2, 251-268

Abstract: SummaryWe study accelerated failure time models in which the survivor function of the additive error term is log-concave. The log-concavity assumption covers large families of commonly used distributions and also represents the aging or wear-out phenomenon of the baseline duration. For right-censored failure time data, we construct semiparametric maximum likelihood estimates of the finite-dimensional parameter and establish the large sample properties. The shape restriction is incorporated via a nonparametric maximum likelihood estimator of the hazard function. Our approach guarantees the uniqueness of a global solution for the estimating equations and delivers semiparametric efficient estimates. Simulation studies and empirical applications demonstrate the usefulness of our method.

Keywords: Accelerate failure time models; nonparametric maximum likelihood estimator (NPMLE); weighted rank estimation; shape restriction (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:23:y:2020:i:2:p:251-268.

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