本帖隐藏的内容

• Admissibility and Completeness
• Decision Theory
• Admissibility of Bayes Rules
• Basic Assumptions
• Existence of Bayes Decision Rules
• Existence of a Minimal Complete Class
• The Separating Hyperplane Theorem
• Essential Completeness of the Class of Nonrandomized Decision Rules
• The Minimax Theorem
• The Complete Class Theorem
• Solving for Minimax Rules
  

• Useful Univariate Distributions
• The Multivariate Normal Distribution
• Sufficient Statistics
• Essentially Complete Classes of Rules Based on Sufficient Statistics
• Exponential Families of Distributions
• Complete Sufficient Statistics
• Continuity of the Risk Function
  

• Invariant Decision Problems
• Invariant Decision Rules
• Admissible and Minimax Invariant Rules
• Location and Scale Parameters
• Minimax Estimates of Location Parameters
• Minimax Estimates for the Parameters of a Normal Distribution
• The Pitman Estimate
• Estimation of a Distribution Function
  

• The Neyman-Pearson Lemma
• Uniformly Most Powerful Tests
• Two-Sided Tests
• Uniformly Most Powerful Unbiased Tests
• Locally Best Tests
• Invariance in Hypothesis Testing
• The Two-Sample Problem
• Confidence Sets
• The General Linear Hypothesis
• Confidence Ellipsoids and Multiple Comparisons
  

• Monotone Multiple Decision Problems
• Bayes Rules in Multiple Decision Problems
• Slippage Problems