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Asymptotic Theory of Nonlinear Regression

Author: Alexander V Ivanov
Publisher: Dordrecht : Springer Netherlands, 1997.
Series: Mathematics and Its Applications, 389.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
This book presents up-to-date mathematical results in asymptotic theory on nonlinear regression on the basis of various asymptotic expansions of least squares, its characteristics, and its distribution functions of functionals of Least Squares Estimator. It is divided into four chapters. In Chapter 1 assertions on the probability of large deviation of normal Least Squares Estimator of regression function parameters  Read more...
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Details

Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Alexander V Ivanov
ISBN: 9789401588775 9401588775
OCLC Number: 851375512
Description: 1 online resource (vi, 330 pages).
Contents: 1 Consistency --
2 Approximation by a Normal Distribution --
3 Asymptotic Expansions Related to the Least Squares Estimator --
4 Geometric Properties of Asymptotic Expansions --
I Subsidiary Facts --
II List of Principal Notations --
Commentary --
1 --
2 --
3 --
4.
Series Title: Mathematics and Its Applications, 389.
Responsibility: by Alexander V. Ivanov.
More information:

Abstract:

This book presents up-to-date mathematical results in asymptotic theory on nonlinear regression on the basis of various asymptotic expansions of least squares, its characteristics, and its distribution functions of functionals of Least Squares Estimator. It is divided into four chapters. In Chapter 1 assertions on the probability of large deviation of normal Least Squares Estimator of regression function parameters are made. Chapter 2 indicates conditions for Least Moduli Estimator asymptotic normality. An asymptotic expansion of Least Squares Estimator as well as its distribution function are obtained and two initial terms of these asymptotic expansions are calculated. Separately, the Berry-Esseen inequality for Least Squares Estimator distribution is deduced. In the third chapter asymptotic expansions related to functionals of Least Squares Estimator are dealt with. Lastly, Chapter 4 offers a comparison of the powers of statistical tests based on Least Squares Estimators. The Appendix gives an overview of subsidiary facts and a list of principal notations. Additional background information, grouped per chapter, is presented in the Commentary section. The volume concludes with an extensive Bibliography. Audience: This book will be of interest to mathematicians and statisticians whose work involves stochastic analysis, probability theory, mathematics of engineering, mathematical modelling, systems theory or cybernetics.

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