Summary

We 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.

中文翻译：

---

具有日志凹入错误的加速故障时间模型

概要

我们研究了加速故障时间模型，其中附加误差项的幸存者函数是对数凹形的。对数凹度假设涵盖了大范围的常用分布，并且还表示基线持续时间的老化或磨损现象。对于右删失时间数据，我们构造了有限维参数的半参数最大似然估计，并建立了大样本属性。通过危害函数的非参数最大似然估计器合并形状限制。我们的方法保证了估计方程全局解决方案的唯一性，并提供了半参数有效估计。仿真研究和经验应用证明了我们方法的有效性。