Parameter estimations in linear failure rate model using masked data

Abstract

Estimations of the parameters included in the lifetime distributions of the individual components in a series system with independent and nonidentical components are introduced in this paper by using masked system life data. Particularly, we deduce the maximum likelihood and Bayes estimates of these parameters when the failure rates of the system components are assumed to be linear with different parameters. In Bayes approach, it is assumed that the unknown parameters to be estimated behave as independent random variables having symmetrical triangular prior distributions. The problem is illustrated on a two-component series system. To show how one can apply the theoretical results obtained here, numerical simulation study is introduced. Also in such simulation study, a comparison between the procedures used is discussed.