Introduction to Statistical Inference (Springer Texts in Statistics)

Kiefer, Jack C.; Gary Lorden (editor)

ISBN 10: 0387964207 / ISBN 13: 9780387964201

Published by Springer-Verlag, N Y, 1987

About this Item

A clean, unmarked book with a tight binding. Light wear and spot on cover. 334 pages. Keywords: Cornell University, Decision Theory, Hypothesis Testing, Confidence Intervals, Parameter Estimation, Mathematical Statistics, Probability Theory, Lecture Notes, Textbook Course, Statistical Methods, Data Analysis, Inferential Stats.

Seller Inventory # 159911

Bibliographic Details

• Title: Introduction to Statistical Inference (Springer Texts in Statistics)
• Publisher: Springer-Verlag, N Y
• Publication Date: 1987
• Language: English
• ISBN 10: 0387964207
• ISBN 13: 9780387964201
• Binding: Hard Cover
• Condition: Very Good
• Dust Jacket Condition: No Jacket
• Seller catalogs:
  • Mathematics

About this title

Synopsis

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.