Probability for Statisticians / Edition 1

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Probability for Statisticians is intended for self-study or as a text for a one-year graduate course aimed especially at students in statist ics. Of particular interest is a presentation of the major central lim it theorems via Stein's method either prior to or alternative to a cha racteristic function presentation. Additionally, there is considerable emphasis placed on the quantile function as well as the distribution function. The bootstrap and trimming are both presented. The martingal e coverage includes coverage of censored data martingales. The text in cludes measure theoretic preliminaries, from which the authors own cou rse typically includes selected coverage.

"...this graduate-level text covers distribution & quantile functions; asymptotics via Stein's approach; characteristic functions, & more. Material is intended for self-study or as a text for a one-year graduate course."

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Editorial Reviews

From the Publisher

From the reviews:

"This book offers a rigorous introduction to measure theoretic probability with particular attention to topics of interest to mathematical statisticians. ... recommended to anyone interseted in the probability underlying modern statistics."
D.L. McLeish in "Short Book Reviews", Vol. 21/1, April 2001

"The book originated from a graduate level course given by the author on probability at the University of Washington, Seattle. It is an excellent textbook for a course in probability for students in mathematical statistics. It provides a solid grounding in the probabilistic tools and techniques that are necessary to do theoretical research in statistics.
For the teaching of probability theory to post graduate statistics students, this is certainly one of the most attractive books available and is highly recommended for that purpose. It is also an extremely good reference source of value to any research statistician.
SASA News, Dec. 2001

"This book contains a wealth of material and is very rigorous. It may serve as a good reference book and as a source for a graduate course in probability. … The author provides detailed notes on the use of the text for a graduate course of probability. … Overall, this is an excellent book to acquire." (Arup Bose, Sankhya, Vol. 64 (1), 2002)

"The present textbook grew out of probability lecturers given at the University of Washington in Seattle. … is an excellent reference. The present monograph can strongly be recommended. It is a highlight in modern probability theory with strong applications to mathematical statistics. It may serve as a textbook for advanced lecturers and seminars but it is also worthwhile as reference book for modern aspects in probability theory. … I am happy to have this book on my desk." (Arnold Janssen, Metrika, April, 2001)

"This is a textbook about probability theory, with a view towards applications in statistics. … The book contains 585 pages of text. It contains a lot of useful material which every theoretical statistician should know. … All in all, this is an interesting book and certainly recommended." (R. Helmers, Kwantitatieve Methoden, Vol. 22 (68), 2001)

"This text covers a broad range of probability theory with special emphasis on fields which are useful for statistics. One characteristic unique to this text is the presentation of different approaches to the CLT. The presentation is in the style: definition – proposition/theorem – proof. More detailed motivations are concentrated in special paragraphs. Questions are included to open the view for further generalizations and motivate the reader to think themselves. Exercises are everywhere." (R. Schlittgen, Zentralblatt MATH, Vol. 951, 2001)

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Product Details

  • ISBN-13: 9780387989532
  • Publisher: Springer New York
  • Publication date: 6/9/2000
  • Series: Springer Texts in Statistics Series
  • Edition description: 2000
  • Edition number: 1
  • Pages: 586
  • Product dimensions: 1.38 (w) x 7.00 (h) x 10.00 (d)

Table of Contents

Measures.- Measurable Functions and Convergence.- Integration.- Derivatives Via Signed Measures.- Measures and Processes on Products.- General Topology and Hilbert Space.- Distribution and Quantile Functions.- Independence and Conditional Distributions.- Special Distributions.- WLLN, SLLN, LIL and Series.- Convergence in Distribution.- Brownian Motion, Embedding and Empirical Processes.- Characteristic Functions.- CLT's Via Characteristic Functions.- Infinitely Divisible and Stable Distributions.- Asymptotics Via Empirical Processes.- Asymptotics Via Stein's Approach.- Martingales.- Convergence on Metric Spaces.

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