APPROXIMATE MLES FOR A SKNEW NORMAL DISTRBUTION UNDER PROGRESSIVE CENSORING

For a skew normel distribution, the maximum likelihood method do not provide explicit estimators for the location and scale parameters based on a progressively Type-II censored sample. This paper provides a simple method of deriving explicit estimators by approximeting the likelihood functions. We study the bias and variance of the maximum likelihood estimators (MLEs) and the approximate estimators and show that the approximation provides estimators that are almost as efficient as the MLEs. We present an example to illustrate this method.

APPROXIMATE MLES FOR A SKNEW NORMAL DISTRBUTION UNDER PROGRESSIVE CENSORING Keywords:

Fisher information , Maximum likelihood estimator , Monte Carlo simulation , Pivotal quantity , Progressively Type-II censoring , Skew normal distribution