The Theory and Applications of Statistical Inference Functions /

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....

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Bibliographic Details
Main Author: McLeish, D. L.
Corporate Author: SpringerLink (Online service)
Other Authors: Small, Christopher G.
Format: eBook
Language:English
Published: New York, NY : Springer New York, 1988.
Series:Lecture notes in statistics (Springer-Verlag) ; 44.
Subjects:
Online Access:Connect to the full text of this electronic book
Description
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 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.
Item Description:Electronic resource.
Physical Description:1 online resource (vi, 124 pages 4 illustrations)
ISBN:9781461238720 (electronic bk.)
1461238722 (electronic bk.)
ISSN:0930-0325 ;