Masked Data Analysis based on the Generalized Linear Model

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.

Keywords:

Bayesian Modeling , Markov chain Monte Carlo Method , Masked Data , Non-ignorable Missing Mechanism