1. Maximum likelihood estimation for left-censored survival times in an additive hazard model.
- Author
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Kremer, Alexander, Weißbach, Rafael, and Liese, Friedrich
- Subjects
- *
MAXIMUM likelihood statistics , *ASYMPTOTIC efficiencies , *MONTE Carlo method , *CONFIDENCE intervals , *PARAMETER estimation , *DATA analysis - Abstract
Abstract: Motivated by an application from finance, we study randomly left-censored data with time-dependent covariates in a parametric additive hazard model. As the log-likelihood is concave in the parameter, we provide a short and direct proof of the asymptotic normality for the maximal likelihood estimator by applying a result for convex processes from Hjort and Pollard (1993). The technique also yields a new proof for right-censored data. Monte Carlo simulations confirm the nominal level of the asymptotic confidence intervals for finite samples, but also provide evidence for the importance of a proper variance estimator. In the application, we estimate the hazard of credit rating transition, where left-censored observations result from infrequent monitoring of rating histories. Calendar time as time-dependent covariates shows that the hazard varies markedly between years. [Copyright &y& Elsevier]
- Published
- 2014
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