The Poisson–Inverse-Gaussian regression model with cure rate: a Bayesian approach and its case influence diagnostics

Adriano Suzuki (), Vicente Cancho and Francisco Louzada

Statistical Papers, 2016, vol. 57, issue 1, 133-159

Abstract: This paper proposes a new survival model, called Poisson Inverse-Gaussian regression cure rate model (PIGcr), which enables different underlying activation mechanisms that lead to the event of interest. The number of competing causes of the event of interest follows a Poisson distribution and the time for the event follows an Inverse-Gaussian distribution. The model takes into account the presence of censored data and covariates. For inferential purposes, a Bayesian approach via Markov Chain Monte Carlo was considered. Discussions on the model selection criteria, as well as a case deletion influence diagnostics are addressed for a joint posterior distribution based on the $$\psi $$ ψ -divergence, which has several divergence measures as particular cases, such as Kullback–Leibler (K–L), $$J$$ J -distance, $$L_1$$ L 1 norm and $$\chi ^2$$ χ 2 -square divergence measures. The procedures are illustrated in artificial and real data. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: Cure fraction models; Inverse-Gaussian distribution; Poisson distribution; Sensitivity analysis; Lifetime data; Bayesian inference (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s00362-014-0649-8

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