Frequentist-Bayes Goodness-of-fit Tests

In this dissertation, the classical problems of testing goodness-of-fit of uniformity and parametric families are reconsidered. A new omnibus test for these problems is proposed and investigated. The new test statistics are a combination of Bayesian and score test ideas. More precisely, singletons that contain only one more parameter than the null describing departures from the null model are introduced.

A Laplace approximation to the posterior probability of the null hypothesis is used, leading to test statistics that are weighted sums of exponentiated squared Fourier coefficients. The weights depend on prior probabilities and the Fourier coefficients are estimated based on score tests. Exponentiation of Fourier components leads to tests that can be exceptionally powerful against high frequency alternatives. Comprehensive simulations show that the new tests have good power against high frequency alternatives and perform comparably to some other well-known omnibus tests at low frequency alternatives.

Asymptotic distributions of the proposed test are derived under null and alternative hypotheses. An application of the proposed test to an interesting real problem is also presented.