Locally quadratic log likelihood and data-based transformations

Ian R. Harris, Donna K. Pauler

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

The investigation of multi-parameter likelihood functions is simplified if the log likelihood is quadratic near the maximum, as then normal approximations to the likelihood can be accurately used to obtain quantities such as likelihood regions. This paper proposes that data-based transformations of the parameters can be employed to make the log likelihood more quadratic, and illustrates the method with one of the simplest bivariate likelihoods, the normal two-parameter likelihood.

Original languageEnglish (US)
Pages (from-to)637-646
Number of pages10
JournalCommunications in Statistics - Theory and Methods
Volume21
Issue number3
DOIs
StatePublished - Jan 1 1992

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Keywords

  • data-based parametrization
  • normal likelihood
  • quadratic log likelihood

ASJC Scopus subject areas

  • Statistics and Probability

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