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On the Relationship between Two Fatigue-Life Models

Anthony F. Desmond

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Abstract

The relationship between two fatigue life distributions, namely the Birnbaum-Saunders and the Inverse Gaussian, is further investigated. An intimate connection exists between the two models, viz, the Birnbaum-Saunders is a mixture of two probability distributions: 1) an Inverse Gaussian random variable, and 2) the reciprocal of an Inverse Gaussian random variable. Advantages and disadvantages of the two distributions are discussed. The arguments favour the Birnbaum-Saunders distribution from a stochastic modeling point of view, whereas the Inverse Gaussian distribution seems to be the more attractive of the two with respect to statistical analysis and analysis of censored data.

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What this paper is about

The relationship between two fatigue life distributions, namely the Birnbaum-Saunders and the Inverse Gaussian, is further investigated. An intimate connection exists between the two models, viz, the Birnbaum-Saunders is a mixture of two probability distributions: 1) an Inverse Gaussian random variable, and 2) the reciprocal of an Inverse Gaussian random variable. Advantages and disadvantages of the two distributions are discussed. The arguments favour the Birnbaum-Saunders distribution from a stochastic modeling point of view, whereas the Inverse Gaussian distribution seems to be the more attractive of the two with respect to statistical analysis and analysis of censored data.

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OpenAlex reports 130 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The relationship between two fatigue life distributions, namely the Birnbaum-Saunders and the Inverse Gaussian, is further investigated. An intimate connection exists between the two models, viz, the Birnbaum-Saunders is a mixture of two probability distributions: 1) an Inverse Gaussian random variable, and 2) the reciprocal of an Inverse Gaussian random variable. Advantages and disadvantages of the two distributions are discussed. The arguments favour the Birnbaum-Saunders distribution from a stochastic modeling point of view, whereas the Inverse Gaussian distribution seems to be the more attractive of the two with respect to statistical analysis and analysis of censored data.

Key concepts: Inverse Gaussian distribution, Generalized inverse Gaussian distribution, Mathematics, Random variable, Gaussian, Inverse distribution, Inverse, Statistical physics

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