Course Contents

The nature and concept of loss function, parameters, decision and sample spaces. Risk and average loss. Admissibility and the classes of admissible decisions. Minimax principle and its application to simple decision problems, linear and quadratic losses and their uses in problems of estimation and testing hypothesis. Asymptotically minimax procedure. A prior distributions and conjugate priors. Bayes decision prodedure, admissibility of Bayes and minimax procedures.