Multiparametric Statistics

This monograph presents the mathematical theory of the sample. In this meaning, the proposed theory can be called "essentially multiparameter". Kolmogorov, asymptotic approach in the sample, increases the number of unknown parameters.

This theory opens a solution for multivariate statistics, which up to now has not been solved. Traditional sampling methods, which can be used in an infinite manner, can be used in an unstable way. In this situation, practical statisticians are forced to find a satisfactory solution.

Mathematical theory developed in this book. Closed systems for experiential linear algebraic equations. It is a remarkable group of populations.

In the conventional situation of small and large sample sizes. It can be expected in the future.

This monograph will be of interest. Mathematicians would be able to be solved in their own regions. Specialists in applied situations. Advantages of the uncertainty and the lack of accuracy.

Large large scientific large scientific scientific scientific scientific scientific A their A scientific A. Large large scientific scientific scientific A scientific A. Students and postgraduates will be interested in the foreground of modern science.

- Presents original mathematical investigations
and open a new branch of mathematical statistics
- Illustrates a technique for the development of large-dimensional problems
- Describes the most popular methods; including algorithms of non-degenerating large-dimensional discriminant and regression analysis

Foreword
Preface
Chapter 1. Introduction: The Development of Multiparametric Statistics
Chapter 2. Fundamental Problem of Statistics
Chapter 3. Spectral Theory of Large Sample Covariance Matrices
Chapter 4. Asymptitically Unimprovable Solution of Multivariate Problems
Chapter 5. Multiparametric Discriminant Analysis
Chapter 6. Theory of Solution is a High-Order Systems of Empirical Linear Algebraic Equations
Appendix
References
index