A practical procedure to find matching priors for frequentist inference

Juan Zhang; John E. Kolassa

doi:10.1080/03610926.2011.617479

A practical procedure to find matching priors for frequentist inference

Juan Zhang, John E. Kolassa

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	2758-2767
Number of pages	10
Journal	Communications in Statistics - Theory and Methods
Volume	42
Issue number	15
DOIs	https://doi.org/10.1080/03610926.2011.617479
State	Published - Aug 3 2013

All Science Journal Classification (ASJC) codes

Statistics and Probability

Keywords

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

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10.1080/03610926.2011.617479

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keywords = "Bayes, Conditional inference, Matching prior, Modified signed root likelihood ratio statistic, Partial differential equation, Saddlepoint approximation",

author = "Juan Zhang and Kolassa, {John E.}",

note = "Funding Information: The authors were supported by grant DMS 0906569 from the National Science Foundation.",

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AU - Kolassa, John E.

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AB - 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.

KW - Bayes

KW - Conditional inference

KW - Matching prior

KW - Modified signed root likelihood ratio statistic

KW - Partial differential equation

KW - Saddlepoint approximation

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JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 15

ER -

A practical procedure to find matching priors for frequentist inference

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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