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Asymptotic Optimal Inference for Non-Ergodic Models: Lecture Notes in Statist...

Description: Asymptotic Optimal Inference for Non-Ergodic Models: Lecture Notes in Statistic 17) [Paperback] Basawa, I. V. and Scott, D. J. Product Overview 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. Read more Details Publisher ‏ : ‎ Springer; First Edition (February 7, 1983) Language ‏ : ‎ English Paperback ‏ : ‎ 183 pages ISBN-10 ‏ : ‎ 0387908102 ISBN-13 ‏ : ‎ 06 Item Weight ‏ : ‎ 9.6 ounces Dimensions ‏ : ‎ 6.1 x 0.43 x 9.25 inches Best Sellers Rank: #8,024,048 in Books (See Top 100 in Books) #1,519 in Stochastic Modeling #8,682 in Statistics (Books) #15,706 in Probability & Statistics (Books) #1,519 in Stochastic Modeling #8,682 in Statistics (Books) We have been selling used books since 2012, and we've learned that the most important thing is doing good business. Honesty is our policy. Free Shipping We ship worldwide. We have multiple warehouses around the world, so please note the extended handling time on certain listings.

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ISBN: 0387908102

ISBN10: 0387908102

ISBN13: 9780387908106

EAN: 9780387908106

MPN: does not apply

Brand: Springer

GTIN: 09780387908106

Number of Pages: Xiii, 170 Pages

Language: English

Publication Name: Asymptotic Optimal Inference for Non-Ergodic Models

Publisher: Springer New York

Subject: Numerical Analysis, Probability & Statistics / General

Publication Year: 1983

Type: Textbook

Item Weight: 10.4 Oz

Author: David John Scott, Ishwar V. Basawa

Item Length: 9.3 in

Subject Area: Mathematics

Item Width: 6.1 in

Series: Lecture Notes in Statistics Ser.

Format: Trade Paperback

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