A practical procedure to find matching priors for frequentist inference

Juan Zhang, John E. Kolassa

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We present a practical way to find matching priors via the use of saddlepoint approximations and obtain p-values of tests of an interest parameter in the presence of nuisance parameters. The advantages of our procedure are the flexibility in choosing different initial conditions so that one may adjust the performance of a test, and the less intensive computational efforts compared to a Markov Chain Monto Carlo method.

Original languageEnglish (US)
Pages (from-to)2758-2767
Number of pages10
JournalCommunications in Statistics - Theory and Methods
Issue number15
StatePublished - Aug 3 2013

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


  • Bayes
  • Conditional inference
  • Matching prior
  • Modified signed root likelihood ratio statistic
  • Partial differential equation
  • Saddlepoint approximation


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