Relative Information Loss Under Type II Censored Exponential Data

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Abstract
This paper uses information theory to quantify information loss in Type II censored samples drawn from an exponential distribution. Indexes of information loss for the maximum likelihood estimation and for Bayesian analysis are defined. Properties of the proposed information measures as functions of the sample size, the exponential parameter, and the parameters of the prior distribution are studied. It is shown that the relative loss of information in Type II censoring may be compensated by increasing the prior precision. Tables of the information loss indexes are provided.

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