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The Theory and Applications of Statistical Inference Functions

Author: D L McLeish; Christopher G Small
Publisher: New York, NY : Springer New York, 1988.
Series: Lecture notes in statistics (Springer-Verlag), 44.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This monograph develops an approach to statistical inference that is both comprehensive in its treatment of statistical principles and sufficiently powerful to be applicable to a variety of important practical problems, such as inference for stochastic processes and classes of estimating functions. Some of the consequences of extending standard concepts of ancillarity, sufficiency and completeness are examined in  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: D L McLeish; Christopher G Small
ISBN: 9781461238720 1461238722
OCLC Number: 852792576
Description: 1 online resource (vi, 124 pages 4 illustrations).
Contents: Introduction --
The Space of Inference Functions: Ancillarity, Sufficiency and Projection --
Selecting an Inference Function for 1-Parameter Models --
Nuisance Parameters --
Inference Under Restrictions --
Inference for Stochastic Processes --
References --
Index.
Series Title: Lecture notes in statistics (Springer-Verlag), 44.
Responsibility: by D.L. McLeish, Christopher G. Small.

Abstract:

This monograph develops an approach to statistical inference that is both comprehensive in its treatment of statistical principles and sufficiently powerful to be applicable to a variety of important practical problems, such as inference for stochastic processes and classes of estimating functions. Some of the consequences of extending standard concepts of ancillarity, sufficiency and completeness are examined in this setting. The development is mathematically mature in its use of Hilbert space methods, but not mathematically difficult. Thus, the construction of this theory is rich in statistical tools for inference without the difficulties found in modern developments, such as likelihood analysis of stochastic processes or higher order methods.

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