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• W2574967310 abstract "The Inverse Gaussian distribution is a plausible model in settings where failure occurs when a deterioration process reaches a certain level. More generally, it is a reasonably flexible two-parameters family of models with properties that are rather similar to those of log-normal distribution. In this paper, an attempt has been made to outline how the Bayesian approach proceeds to fit such a model using Laplace Approximation, which requires the ability to maximize the joint log-posterior density. This model is applied to a real survival censored data, so that, all the concepts and computations will be around that data. R code has been developed and provided to implement in censored mechanism using analytic Laplace Approximation method. Moreover, parallel simulations tools are also implemented with an extensive use of R." @default.
• W2574967310 created "2017-01-26" @default.
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• W2574967310 date "2016-01-01" @default.
• W2574967310 modified "2023-10-16" @default.
• W2574967310 title "A Bayesian Approach to Survival Analysis of Inverse Gaussian Model with Laplace Approximation" @default.
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• W2574967310 hasPublicationYear "2016" @default.
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