Simulation methods for mean and median bias reduction in parametric estimation

J. Cabrera, G. S. Watson

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The use of the iterated Bootstrap to find estimators that have the correct expectations is now standard. However when the distributions are skewed, or without means, the median makes more sense to us. This paper is primarily concerned with an algorithm that produces estimators whose median equals the unknown parameter. The method is illustrated by its application to four troublesome parametric estimation problems and a dataset.

Original languageEnglish (US)
Pages (from-to)143-152
Number of pages10
JournalJournal of Statistical Planning and Inference
Volume57
Issue number1
DOIs
StatePublished - Jan 15 1997

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Keywords

  • Bootstrap
  • Estimating equations

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