Fieller's theorem, the likelihood and the delta method

Research output: Contribution to journalArticlepeer-review

Abstract

This note discusses the direct likelihood estimation of ratio parameters. The method is based on the use of nonlinear regression models from the exponential family. The variances of the maximum likelihood estimates are identical with values obtained from the corresponding generalized linear models and the delta method (Bishop, Fienberg, and Holland, Discrete Multivariate Analysis: Theory and Practice, Cambridge, Massachusetts: MIT Press, 1975), but are easier to calculate. The approach is illustrated by means of a number of examples, including a rather complex logistic regression model. We also discuss the connection between confidence intervals obtained from Fieller's theorem and large-sample intervals obtained from the information matrix. We conclude from this comparison and from the examples that direct maximum likelihood estimation of ratios offers a useful alternative to traditional methods based on linear models, especially for complex data sets.

Original languageEnglish (US)
Pages (from-to)709-718
Number of pages10
JournalBiometrics
Volume46
Issue number3
DOIs
StatePublished - Jan 1 1990
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Fieller's theorem, the likelihood and the delta method'. Together they form a unique fingerprint.

Cite this