Point estimation with sequential order statistics from exponential distributions

The model of sequential order statistics is motivated by the structure of a k-out-of-n system. Such a system consists of n components and is intact, if at least k or more components work. The lifetime of a k-out-of-n system is described by the (n-k+1)-th ordinary order statistic. In modeling a k-out-of-n system it is usually assumed that the components work independently. But this assumption is possibly not fulfilled for some systems, if the failure of a component stresses or even damages the remaining components. In such a situation some sort of dependence structure should be taken into account. Kamps (1995) introduced a sequential k-out-of-n system, where, after the occurrence of a failure, the lifetime distributions of the remaining components are parametrically adjusted. These adjustments are described by numerical values of the model parameters. A major concern of this thesis is to derive the joint density function of a multiply type II censored sample from sequential order statistics when the underlying lifetime distribution is assumed to be a twoparameter exponential distribution. We estimate the distribution parameters, as well as the model parameters using Bayes-, best linear unbiased-, quasi-likelihood-, maximum likelihood-, and minimum variance techniques, where the focus is on deriving explicit representations of the corresponding estimators.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Universität Oldenburg

Year

2001

Author dc:creator

• Schenk, Normen

• [Keine Schlagwörter von Autor/in vergeben.]

Repository record source_url

http://oops.uni-oldenburg.de/299

OAI identifier oai:identifier

oai:oops.uni-oldenburg.de:299