Maximum likelihood estimation in a weibull regression model with type-1 censoring: A monte carlo study

T. Elperin, I. Gertsbakh

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Results of the Monte Carlo study of the performance of a maximum likelihood estimation in a Weibull parametric regression model with two explanatory variables are presented. One simulation run contained 1000 samples censored on the average by the amount of 0-30%. Each simulated sample was generated in a form of two-factor two-level balanced experiment. The confidence intervals were computed using the large-sample normal approximation via the matrix of observed information. For small sample sizes the estimates of the scale parameter b of the loglifetime were significantly negatively biased, which resulted in a poor quality of confidence intervals for b and the low-level quantiles. All estimators improved their quality when the nominal value of b decreased. A moderate amount of censoring improved the quality of point and confidence estimation. The reparametrization b = ✓b1 produced rather accurate confidence intervals. Exact confidence intervals for b in case of non-censoring were obtained using the pivotal quantity b/b.

Original languageEnglish
Pages (from-to)349-371
Number of pages23
JournalCommunications in Statistics Part B: Simulation and Computation
Issue number2
StatePublished - 1 Jan 1987


  • Nonmal Large-Sample Approximation
  • Parametric Regression
  • Point and Confidence Estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation


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