Noninformative priors based on asymptotic likelihood methods.
Published: 2005
Language: eng
Number of pages: 98
Description: Many Bayesian analysis are performed with non-informative priors. Non-informative priors are often regarded as default priors in practice. For a one-parameter location model, the Bayesian survivor function will agree with the frequentist p-value using a uniform prior. Recently developed asymptotic likelihood methods give a third order approximate location model which agrees with the given continuous model to third order. The location parameterization can be used to define a uniform prior. When the parameter of interest is not a linear function of the location parameter, then using a uniform prior under the location parameterization will not give strong agreement between Bayesian and frequentist inference. We give an algorithm to find contours in the original parameter space corresponding to a constant value of a linear location parameter and illustrate it with the normal model.We propose two priors for the parameter of interest based on second order and third order location parameterizations for a scalar parameter of interest in the presence of nuisance parameters. The posteriors for the parameter of interest are obtained from combining a modified profile likelihood with the proposed priors. Some examples are given to compare the p-values and the Bayesian survivor functions.
Notes: Thesis (Ph.D.)--University of Toronto, 2005.

Electronic version licensed for access by U. of T. users.

Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3216.