Confidence-credible intervals

Frequentist and Bayesian approaches for interval estimation usually produce conflicting results if applied to analyze the same data set. Paradoxically, there is no unanimity in the literature on whether frequentist and Bayesian approaches are indeed concurrent theories. Thus, a fundamental question arises: frequentist and Bayesian approaches for interval estimation could be somehow reconciled? This paper offers an affirmative response for this question. Furthermore, we introduce a reconciling solution based on a hybrid frequentist-Bayesian interval estimator, the 'confidence-credible interval'. The hybrid approach is simple and intuitive. It is also comprehensive in the sense of being applicable for any data probability distribution/likelihood shape, and for arbitrary prior distributions. An intensive simulation study shows the performance of the new methodology for the Gaussian and the Gamma distributions. The proposed method is illustrated through an application using real data in the light of state space models.