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   Masked Data Analysis Based on the Generalized Linear Model  
   
نویسنده Misaii Hasan ,Haghighi Firoozeh ,Eftekhari Mahabadi Samaneh
منبع International Journal Of Reliability, Risk And Safety: Theory And Application - 2020 - دوره : 3 - شماره : 2 - صفحه:1 -7
چکیده    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.
کلیدواژه Bayesian Modeling ,Markov Chain Monte Carlo Method ,Masked Data ,Non-Ignorable Missing Mechanism
آدرس University OfTehran, School Of Mathematics, Statistics And Computer Science, College Of Science, Iran, University OfTehran, School Of Mathematics, Statistics And Computer Science, College Of Science, Iran, University OfTehran, School Of Mathematics, Statistics And Computer Science, College Of Science, Iran
پست الکترونیکی s.eftekhari@khayam.ut.ac.ir
 
     
   
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