
Contiguity of Probability Measures
Some Applications in Statistics
George G. Roussas(Author)
Cambridge University Press
Published on 27. November 2008
Book
Paperback/Softback
264 pages
978-0-521-09095-7 (ISBN)
Description
This Tract presents an elaboration of the notion of 'contiguity', which is a concept of 'nearness' of sequences of probability measures. It provides a powerful mathematical tool for establishing certain theoretical results with applications in statistics, particularly in large sample theory problems, where it simplifies derivations and points the way to important results. The potential of this concept has so far only been touched upon in the existing literature, and this book provides the first systematic discussion of it. Alternative characterizations of contiguity are first described and related to more familiar mathematical ideas of a similar nature. A number of general theorems are formulated and proved. These results, which provide the means of obtaining asymptotic expansions and distributions of likelihood functions, are essential to the applications which follow.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Product notice
Paperback (trade)
Illustrations
Worked examples or Exercises
Dimensions
Height: 216 mm
Width: 140 mm
Thickness: 16 mm
Weight
377 gr
ISBN-13
978-0-521-09095-7 (9780521090957)
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Schweitzer Classification
Other editions
Additional editions
Book
10/1972
Cambridge University Press
€6.19
Article exhausted; check for reprint
Previous edition
Book
10/1972
Cambridge University Press
€6.19
Article exhausted; check for reprint
Content
1. On the concept of contiguity and related theorems; 2. Asymptotic expansion and asymptotic distribution of likelihood functions; 3. Approximation of a given family of probability measures by an exponential family - asymptotic sufficiency; 4. Some statistical applications: AUMP and AUMPU tests for certain testing hypotheses problems; 5. Some statistical applications: asymptotic efficiency of estimates; 6. Multiparameter asymptotically optimal tests.