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Maximum likelihood estimation for left-censored survival times in an additive hazard model.
- Source :
-
Journal of Statistical Planning & Inference . Jun2014, Vol. 149, p33-45. 13p. - Publication Year :
- 2014
-
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]
Details
- Language :
- English
- ISSN :
- 03783758
- Volume :
- 149
- Database :
- Academic Search Index
- Journal :
- Journal of Statistical Planning & Inference
- Publication Type :
- Academic Journal
- Accession number :
- 95930811
- Full Text :
- https://doi.org/10.1016/j.jspi.2014.02.013