Kernel density estimation of three-parameter Weibull distribution with neural network and genetic algorithm

Fan Yang, Zhufeng Yue

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Three-parameter Weibull distribution is widely employed as a model in reliability and lifetime studies due to its good fit to data. It is important to estimate the unknown parameters exactly for modeling. There are many methods to estimate the parameters of three-parameter Weibull distribution and the kernel density estimation method is one of them. The smoothing parameter has a significant influence on the estimation accuracy. In this paper, the neural network and genetic algorithm were used to get the best smoothing parameter and the result was compared with other methods. The Monte Carlo simulations were carried out to show the feasibility of our approach for estimation of three-parameter Weibull distribution.

Original languageEnglish
Pages (from-to)803-814
Number of pages12
JournalApplied Mathematics and Computation
Volume247
DOIs
StatePublished - 15 Nov 2014

Keywords

  • Genetic algorithm
  • Grey model
  • Maximum likelihood method
  • Neural network model
  • Optimization algorithm
  • Weibull distribution

Fingerprint

Dive into the research topics of 'Kernel density estimation of three-parameter Weibull distribution with neural network and genetic algorithm'. Together they form a unique fingerprint.

Cite this