Advances and Applications in Statistics
Volume 14, Issue 1, Pages 31 - 47
(January 2010)
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A NON-PARAMETRIC NHPP SOFTWARE RELIABILITY MODEL
May Barghout
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Abstract: This paper addresses a family of probability models for the failure time process known as Non-Homogeneous Poisson Process (NHPP) models. Conventional NHPP models make rather strong distributional assumptions about the detection times: typically they assume that these come from some parametric family of distributions. Recently non-parametric models were developed in an attempt to relax these assumptions and - in the tradition of non-parametric statistics generally - ‘allow the data to speak for themselves’. These models were based on fixed-width kernel density estimators. We present a new non-parametric model for reliability prediction which is based upon the use of adaptive kernel density estimators. Its predictive accuracy is compared on some real data sets with the predictions that come from the fixed-width kernel density estimators. The initial results are encouraging. |
Keywords and phrases: software reliability growth, non-parametric estimation, kernel density estimation, adaptive kernel density estimation, NHPP models. |
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