Abstract
Abtsrcat
The classical approach of stochastic modeling is to first guess a model and then to validate the same using observed data and some test for goodness of fit. Unfortunately, this is not a very rational approach. The initial choice of model becomes a subjective one, some goodness of fit test may demand for large number of observations and difierent models may fit well for the same data set. Given these limitations of the classical approach, one may, as an alternative, make use of characterization results. At the same time, to take care of the difficulties in verification of characterizing properties one needs stability properties as well.
Stability results have been mostly studied for the exponential distribution. So far as non-exponential distributions are concerned, one may refer to only few works. The present study deals with stability properties in terms of Matusita distance for characterization results involving reliability measures. These results are generic in nature and can be used for the distribution cor- responding to any non-negative random variable.
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