Parameter estimation of the Pareto distribution using a pivotal quantity

Joseph H.T. Kim, Sanghyun Ahn, Soohan Ahn

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


In estimating the parameters of the two-parameter Pareto distribution it is well known that the performance of the maximum likelihood estimator deteriorates when sample sizes are small or the underlying model is contaminated. In this paper we propose a new parameter estimator that utilizes a pivotal quantity based on the regression framework, allowing separate estimation of the two parameters in a straightforward manner. The consistency of the estimator is also established. Simulation studies show that the proposed estimator is a competitive, well-rounded robust estimator for both Pareto and contaminated Pareto datasets when the sample sizes are small.

Original languageEnglish
Pages (from-to)438-450
Number of pages13
JournalJournal of the Korean Statistical Society
Issue number3
StatePublished - 1 Sep 2017


  • Parameter estimation
  • Pareto distribution
  • Robust estimation
  • Weighted linear regression


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