On the Asymptotics of Maximum Likelihood and Related Estimators Based on Type II Censored Data.
Technical summary rept.,
WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER
Pagination or Media Count:
Some simple procedures are provided for establishing the asymptotic normality and uniform strong convergence of a class of functions that arise in the context of estimating parameters from a type II censored sample. These are used to streamline and strengthen the traditional treatment of the asymptotic theory of maximum likelihood estimators based on censored data. Further applications include the treatment of asymptotics of some modified maximum likelihood MML estimators. In particular, conditions are provided for the consistency and limiting normality of the MML estimators of Mehrotra and Nanda, and the asymptotic efficiencies of these estimators are evaluated. Author
- Numerical Mathematics