Using BBPSO algorithm to estimate the Weibull parameters with censored data

Fu Kwun Wang*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

13 Scopus citations

Abstract

This article proposes the maximum likelihood estimates based on bare bones particle swarm optimization (BBPSO) algorithm for estimating the parameters of Weibull distribution with censored data, which is widely used in lifetime data analysis. This approach can produce more accuracy of the parameter estimation for the Weibull distribution. Additionally, the confidence intervals for the estimators are obtained. The simulation results show that the BB PSO algorithm outperforms the Newton-Raphson method in most cases in terms of bias, root mean square of errors, and coverage rate. Two examples are used to demonstrate the performance of the proposed approach. The results show that the maximum likelihood estimates via BBPSO algorithm perform well for estimating the Weibull parameters with censored data.

Original languageEnglish
Pages (from-to)2614-2627
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Volume43
Issue number10
DOIs
StatePublished - 2014
Externally publishedYes

Keywords

  • Aximum likelihood estimation
  • Bare bones particle swarm optimization
  • Censored data
  • Weibull distribution

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