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.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Conditional inference
- Matching prior
- Modified signed root likelihood ratio statistic
- Partial differential equation
- Saddlepoint approximation