A Goodness-of-Identifiability Criterion for Parametric Statistical Models

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Abstract

This note sets out a goodness-of-identifiability criterion. This criterion quantifies the identifying strength of a parametric statistical model. Unlike the qualitative
criterion for identifiability based only on the Fisher matrix, it applies to both regular and irregular points of the Fisher matrix. Unlike the qualitative criterion based only on the Hellinger distance, it quantifies set-identification.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalJournal of Statistical and Econometric Methods
Volume11
Issue number4
DOIs
Publication statusPublished - 24 Oct 2022

Keywords

  • Parametric Statistical Models
  • IDENTIFIABILITY
  • Fisher matrix
  • Hellinger Distance

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