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Statistical Analysis of Observations of Increasing Dimension

Author: V L Girko
Publisher: Dordrecht : Springer Netherlands, 1995.
Series: Theory and decision library., Series B,, Mathematical and statistical methods ;, 28.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Statistical Analysis of Observations of Increasing Dimension is devoted to the investigation of the limit distribution of the empirical generalized variance, covariance matrices, their eigenvalues and solutions of the system of linear algebraic equations with random coefficients, which are an important function of observations in multidimensional statistical analysis. A general statistical analysis is developed in  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: V L Girko
ISBN: 9789401585675 9401585679
OCLC Number: 851380524
Description: 1 online resource (xxi, 290 pages).
Contents: 1. Introduction to General Statistical Analysis --
2. Limit Theorems for the Empirical Generalized Variance --
3. The Canonical Equations C1 ..., C3 for the Empirical Covariance Matrix --
4. Limit Theorems for the Eigenvalues of Empirical Covariance Matrices --
5. G2-estimator for the Stieltjes Transform of the Normalized Spectral Function of Covariance Matrices --
6. Statistical Estimators for Solutions of Systems of Linear Algebraic Equations --
References.
Series Title: Theory and decision library., Series B,, Mathematical and statistical methods ;, 28.
Responsibility: by Vyacheslav L. Girko.
More information:

Abstract:

Statistical Analysis of Observations of Increasing Dimension is devoted to the investigation of the limit distribution of the empirical generalized variance, covariance matrices, their eigenvalues and solutions of the system of linear algebraic equations with random coefficients, which are an important function of observations in multidimensional statistical analysis. A general statistical analysis is developed in which observed random vectors may not have density and their components have an arbitrary dependence structure. The methods of this theory have very important advantages in comparison with existing methods of statistical processing. The results have applications in nuclear and statistical physics, multivariate statistical analysis in the theory of the stability of solutions of stochastic differential equations, in control theory of linear stochastic systems, in linear stochastic programming, in the theory of experiment planning.

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