Accelerated failure time models with log-concave errors. Issue 2 (6th December 2019)

• Record Type: Journal Article
• Authors: Liu, Ruixuan; Yu, Zhengfei
• Is Part Of: Econometrics journal. Volume 23:Issue 2(2020)
• Page Start: 251
• Page End: 268
• Publication Date: 2019-12-06
• Subjects: Accelerate failure time models -- nonparametric maximum likelihood estimator (NPMLE) -- weighted rank estimation -- shape restriction; Econometrics -- Periodicals; 330.015195
• DOI: 10.1093/ectj/utz024
• Languages: English
• ISSNs: 1368-4221

Abstract

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.