A practical procedure to find matching priors for frequentist inference
  In the manuscript, we present a practical way to find the matching priors proposed by Welch & Peers (1963) and Peers (1965). We investigate the use of saddlepoint approximations combined with matching priors and obtain p-values of the test of an interest parameter in the presence of nuisance parameter. The advantage of our procedure is the flexibility of choosing different initial conditions so that one can adjust the performance of the test. Two examples have been studied, with coverage verified via Monte Carlo simulation. One relates to the ratio of two exponential means, and the other relates the logistic regression model. Particularly, we are interested in small sample settings. 
Juan Zhang and John E. Kolassa