Introduction to Statistical Inference

1. Front Matter

   Pages i-viii

   PDF

2. Introduction to Statistical Inference

   • Jack Carl Kiefer

   Pages 1-3

3. Specification of a Statistical Problem

   • Jack Carl Kiefer

   Pages 4-22

4. Classifications of Statistical Problems

   • Jack Carl Kiefer

   Pages 23-30

5. Some Criteria For Choosing a Procedure

   • Jack Carl Kiefer

   Pages 31-80

6. Linear Unbiased Estimation

   • Jack Carl Kiefer

   Pages 81-136

7. Sufficiency

   • Jack Carl Kiefer

   Pages 137-157

8. Point Estimation

   • Jack Carl Kiefer

   Pages 158-245

9. Hypothesis Testing

   • Jack Carl Kiefer

   Pages 246-286

10. Confidence Intervals

    • Jack Carl Kiefer

    Pages 287-311

11. Back Matter

    Pages 312-334

    PDF

This book is based upon lecture notes developed by Jack Kiefer for a course in statistical inference he taught at Cornell University. The notes were distributed to the class in lieu of a textbook, and the problems were used for homework assignments. Relying only on modest prerequisites of probability theory and cal­ culus, Kiefer's approach to a first course in statistics is to present the central ideas of the modem mathematical theory with a minimum of fuss and formality. He is able to do this by using a rich mixture of examples, pictures, and math­ ematical derivations to complement a clear and logical discussion of the important ideas in plain English. The straightforwardness of Kiefer's presentation is remarkable in view of the sophistication and depth of his examination of the major theme: How should an intelligent person formulate a statistical problem and choose a statistical procedure to apply to it? Kiefer's view, in the same spirit as Neyman and Wald, is that one should try to assess the consequences of a statistical choice in some quan­ titative (frequentist) formulation and ought to choose a course of action that is verifiably optimal (or nearly so) without regard to the perceived "attractiveness" of certain dogmas and methods.

• Likelihood
• Mathematica
• Statistica
• Variance
• class
• derivation
• equality
• form
• geometry
• minimum
• presentation
• probability
• statistical inference
• statistics
• testing

• California Institute of Technology, Pasadena, USA

  Gary Lorden

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