A log-linear regression model for the odd Weibull distribution with censored data

Edwin M.M. Ortega, Gauss M. Cordeiro, Elizabeth M. Hashimoto, Kahadawala Cooray

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


We introduce the log-odd Weibull regression model based on the odd Weibull distribution (Cooray, 2006). We derive some mathematical properties of the log-transformed distribution. The new regression model represents a parametric family of models that includes as sub-models some widely known regression models that can be applied to censored survival data. We employ a frequentist analysis and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to assess global influence. Further, for different parameter settings, sample sizes and censoring percentages, some simulations are performed. In addition, the empirical distribution of some modified residuals are given and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to check the model assumptions. The extended regression model is very useful for the analysis of real data.

Original languageEnglish
Pages (from-to)1859-1880
Number of pages22
JournalJournal of Applied Statistics
Issue number9
StatePublished - Sep 2014


  • censored data
  • diagnostic analysis
  • odd Weibull distribution
  • regression model
  • residual analysis
  • survival function


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