Minimum message length inference of the Weibull distribution with complete and censored data

TL;DR

This paper introduces a minimum message length Bayesian approach for estimating Weibull distribution parameters, demonstrating its superiority over traditional maximum likelihood methods especially with censored data.

Contribution

It develops a novel minimum message length inference method for Weibull parameters, including censored data, outperforming existing estimation techniques.

Findings

MML estimates outperform MLE in small samples and high censoring.

Proposed method reduces Kullback-Leibler risk compared to other estimates.

Extension to Type II censored data is derived.

Abstract

The Weibull distribution, with shape parameter $k>0$ and scale parameter $\lambda>0$, is one of the most popular parametric distributions in survival analysis with complete or censored data. Although inference of the parameters of the Weibull distribution is commonly done through maximum likelihood, it is well established that the maximum likelihood estimate of the 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 complete data or data with type I censoring. Empirical experiments show that the proposed minimum message length estimate of the shape parameter is superior to the maximum likelihood estimate and appears superior to other recently proposed modified maximum likelihood estimates in terms of Kullback-Leibler risk. Lastly, we derive an extension of the proposed method to data with type II censoring.