Statistical Decisions Using Likelihood Information Without Prior
  Probabilities
  This paper presents a decision-theoretic approach to statistical inference
that satisfies the likelihood principle (LP) without using prior information.
Unlike the Bayesian approach, which also satisfies LP, we do not assume
knowledge of the prior distribution of the unknown parameter. With respect to
information that can be obtained from an experiment, our solution is more
efficient than Wald's minimax solution.However, with respect to information
assumed to be known before the experiment, our solution demands less input than
the Bayesian solution.
