Discrimination procedures for separate families of hypotheses

Alan R. Dyer*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Procedures for discriminating between models from separate families of hypotheses are examined, with principal emphasis on procedures invariant under location and scale transformations. The discrimination procedures considered are compared using Monte Carlo samples as data for five pairs of invariant distributions. These comparisons are made with respect to the best invariant procedure, a procedure requiring no knowledge of sampling distributions, using approximate relative efficiencies calculated from the Monte Carlo results. On the basis of these efficiencies and the computational complexities of the procedures, suggestions are made for their use.

Original languageEnglish (US)
Pages (from-to)970-974
Number of pages5
JournalJournal of the American Statistical Association
Volume68
Issue number344
DOIs
StatePublished - Dec 1973

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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