A new class of survival regression models with heavy-tailed errors: Robustness and diagnostics

Michelli Barros, Gilberto A. Paula, VICTOR ELISEO LEIVA SANCHEZ

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

Abstract

Birnbaum-Saunders models have largely been applied in material fatigue studies and reliability analyses to relate the total time until failure with some type of cumulative damage. In many problems related to the medical field, such as chronic cardiac diseases and different types of cancer, a cumulative damage caused by several risk factors might cause some degradation that leads to a fatigue process. In these cases, BS models can be suitable for describing the propagation lifetime. However, since the cumulative damage is assumed to be normally distributed in the BS distribution, the parameter estimates from this model can be sensitive to outlying observations. In order to attenuate this influence, we present in this paper BS models, in which a Student-t distribution is assumed to explain the cumulative damage. In particular, we show that the maximum likelihood estimates of the Student-t log-BS models attribute smaller weights to outlying observations, which produce robust parameter estimates. Also, some inferential results are presented. In addition, based on local influence and deviance component and martingale-type residuals, a diagnostics analysis is derived. Finally, a motivating example from the medical field is analyzed using log-BS regression models. Since the parameter estimates appear to be very sensitive to outlying and influential observations, the Student-t log-BS regression model should attenuate such influences. The model checking methodologies developed in this paper are used to compare the fitted models.

Original languageEnglish
Pages (from-to)316-332
Number of pages17
JournalLifetime Data Analysis
Volume14
Issue number3
DOIs
StatePublished - 1 Sep 2008

Keywords

  • Generalized Birnbaum-Saunders distribution
  • Likelihood methods
  • Local influence
  • Log-linear models
  • Residual analysis
  • Robustness
  • Sinh-normal distribution

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