| Statistical Analysis of Observations of Increasing Dimension 1995 Edition Contributor(s): Girko, V. L. (Author) |
|
 |
ISBN: 0792328868 ISBN-13: 9780792328865 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: March 1995 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Multivariate Analysis - Mathematics | Algebra - Linear |
| Dewey: 519.535 |
| LCCN: 95005546 |
| Series: Theory and Decision Library B |
| Physical Information: 0.75" H x 6.14" W x 9.21" (1.36 lbs) 290 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |