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Asymptotic Optimal Inference for Non-ergodic Models (Paperback, Softcover reprint of the original 1st ed. 1983)
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Asymptotic Optimal Inference for Non-ergodic Models (Paperback, Softcover reprint of the original 1st ed. 1983)
Series: Lecture Notes in Statistics, 17
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This monograph contains a comprehensive account of the recent work
of the authors and other workers on large sample optimal inference
for non-ergodic models. The non-ergodic family of models can be
viewed as an extension of the usual Fisher-Rao model for
asymptotics, referred to here as an ergodic family. The main
feature of a non-ergodic model is that the sample Fisher
information, appropriately normed, converges to a non-degenerate
random variable rather than to a constant. Mixture experiments,
growth models such as birth processes, branching processes, etc. ,
and non-stationary diffusion processes are typical examples of
non-ergodic models for which the usual asymptotics and the
efficiency criteria of the Fisher-Rao-Wald type are not directly
applicable. The new model necessitates a thorough review of both
technical and qualitative aspects of the asymptotic theory. The
general model studied includes both ergodic and non-ergodic
families even though we emphasise applications of the latter type.
The plan to write the monograph originally evolved through a series
of lectures given by the first author in a graduate seminar course
at Cornell University during the fall of 1978, and by the second
author at the University of Munich during the fall of 1979. Further
work during 1979-1981 on the topic has resolved many of the
outstanding conceptual and technical difficulties encountered
previously. While there are still some gaps remaining, it appears
that the mainstream development in the area has now taken a more
definite shape.
General
Imprint: |
Springer-Verlag New York
|
Country of origin: |
United States |
Series: |
Lecture Notes in Statistics, 17 |
Release date: |
February 1983 |
First published: |
1983 |
Authors: |
I.V. Basawa
• D J Scott
|
Dimensions: |
235 x 155 x 10mm (L x W x T) |
Format: |
Paperback
|
Pages: |
170 |
Edition: |
Softcover reprint of the original 1st ed. 1983 |
ISBN-13: |
978-0-387-90810-6 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
0-387-90810-2 |
Barcode: |
9780387908106 |
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