Abstract
In this paper, we consider the estimation problem in the presence of masked data for series systems. A missing indicator is proposed to describe masked set of each failure time.Moreover, a Generalized Linear model (GLM) with appropriate link function is used to model masked indicator in order to involve masked information into likelihood function. Both maximum likelihood and Bayesian methods were considered.The likelihood function with both missing at random (MAR) and missing not at random (MNAR) mechanismsare derived.Using an auxiliary variable, a Bayesian approach is expanded to obtain posterior estimations of the model parameters.The proposed methods have been illustrated through a real example.
Highlights
Introduction*The failure time and the exact component that causes system failure are important and can be used to estimate the reliability of component and system
In this paper, we consider the estimation problem in the presence of masked data for series systems
The problem of maximum likelihood estimates (MLE) in the presence of masked data has been considered by some authors such as Miyakawa [1], Usher and Hodgson [4] and Lin et al [5], while Reiser [6], Berger and Sun [7],Mukhopadhyay and Basu[8] and Cai et al [9] studied Bayesian statistical inference under masked data
Summary
The failure time and the exact component that causes system failure are important and can be used to estimate the reliability of component and system. Xu et al [19] presented a Bayesian approach for masked data in step stress accelerated life testing and considered log-location-scale distribution family for their study There is another type of incomplete data called missing data. EftekhariMahabadi and missing values, missing mechanism is called missing not at random (MNAR) (Little \& Rubin, 2002) In this work, both classic and Bayesian statistical inference in the presence of masked data has been studied. A generalized linear model (GLM)with appropriate link function is used to model missing indicator and it is involved into the likelihood function This method allows to analyse masked data in a new manner which is more flexible than existing approach, specially when using of Bayesian method is desired.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Reliability, Risk and Safety: Theory and Application
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
