Minimum Message Length Inference of the Weibull Distribution with Complete and Censored Data

The Weibull distribution, with shape parameter \(k>0\) and scale parameter \(\lambda >0\) , is one of the most popular parametric distributions in survival analysis. It is well established that the maximum likelihood estimate of the Weibull shape parameter is inadequate due to the associated large bias when the sample size is small or the proportion of censored data is large. This manuscript demonstrates how the Bayesian information-theoretic minimum message length principle, coupled with a suitable choice of weakly informative prior distributions, can be used to infer Weibull distribution parameters given either complete data or data with censoring. Empirical experiments show that the proposed minimum message length estimate of the shape parameter is superior to the maximum likelihood estimate and is competitive with other recently proposed modified maximum likelihood estimates in terms of Kullback-Leibler risk.