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Multiparametric statistics[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.535 22
書名/作者:	Multiparametric statistics/ Vadim I. Serdobolskii.
作者:	Serdobolskii, V.
出版者:	Amsterdam ; : Elsevier,, c2008.
面頁冊數:	1 online resource (xvii, 315 p.)
附註:	Title from e-book title screen (viewed Jan. 16, 2008).
標題:	Multivariate analysis.
ISBN:	9786611096298
ISBN:	6611096299
ISBN:	9780444530493
ISBN:	0444530495
書目註:	Includes bibliographical references and index.
內容註:	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 to High-Order Systems of Empirical Linear Algebraic Equations -- Appendix -- References -- Index.
摘要、提要註:	This monograph presents mathematical theory of statistical models described by the essentially large number of unknown parameters, comparable with sample size but can also be much larger. In this meaning, the proposed theory can be called "essentially multiparametric". It is developed on the basis of the Kolmogorov asymptotic approach in which sample size increases along with the number of unknown parameters. This theory opens a way for solution of central problems of multivariate statistics, which up until now have not been solved. Traditional statistical methods based on the idea of an infinite sampling often break down in the solution of real problems, and, dependent on data, can be inefficient, unstable and even not applicable. In this situation, practical statisticians are forced to use various heuristic methods in the hope the will find a satisfactory solution. Mathematical theory developed in this book presents a regular technique for implementing new, more efficient versions of statistical procedures. Near exact solutions are constructed for a number of concrete multi-dimensional problems: estimation of expectation vectors, regression and discriminant analysis, and for the solution to large systems of empiric linear algebraic equations. It is remarkable that these solutions prove to be not only non-degenerating and always stable, but also near exact within a wide class of populations. In the conventional situation of small dimension and large sample size these new solutions far surpass the classical, commonly used consistent ones. It can be expected in the near future, for the most part, traditional multivariate statistical software will be replaced by the always reliable and more efficient versions of statistical procedures implemented by the technology described in this book. This monograph will be of interest to a variety of specialists working with the theory of statistical methods and its applications. Mathematicians would find new classes of urgent problems to be solved in their own regions. Specialists in applied statistics creating statistical packages will be interested in more efficient methods proposed in the book. Advantages of these methods are obvious: the user is liberated from the permanent uncertainty of possible instability and inefficiency and gets algorithms with unimprovable accuracy and guaranteed for a wide class of distributions. A large community of specialists applying statistical methods to real data will find a number of always stable highly accurate versions of algorithms that will help them to better solve their scientific or economic problems. Students and postgraduates will be interested in this book as it will help them get at the foremost frontier of modern statistical science. - Presents original mathematical investigations and open a new branch of mathematical statistics - Illustrates a technique for developing always stable and efficient versions of multivariate statistical analysis for large-dimensional problems - Describes the most popular methods some near exact solutions; including algorithms of non-degenerating large-dimensional discriminant and regression analysis.
電子資源:	http://www.myilibrary.com?id=109629
電子資源:	An electronic book accessible through the World Wide Web; click for information

Multiparametric statistics[electronic resource] /
Serdobolskii, V.

Multiparametric statistics[electronic resource] /Vadim I. Serdobolskii. - Amsterdam ;Elsevier,c2008. - 1 online resource (xvii, 315 p.)

Title from e-book title screen (viewed Jan. 16, 2008).

Includes bibliographical references and index.

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 to High-Order Systems of Empirical Linear Algebraic Equations -- Appendix -- References -- Index.

This monograph presents mathematical theory of statistical models described by the essentially large number of unknown parameters, comparable with sample size but can also be much larger. In this meaning, the proposed theory can be called "essentially multiparametric". It is developed on the basis of the Kolmogorov asymptotic approach in which sample size increases along with the number of unknown parameters. This theory opens a way for solution of central problems of multivariate statistics, which up until now have not been solved. Traditional statistical methods based on the idea of an infinite sampling often break down in the solution of real problems, and, dependent on data, can be inefficient, unstable and even not applicable. In this situation, practical statisticians are forced to use various heuristic methods in the hope the will find a satisfactory solution. Mathematical theory developed in this book presents a regular technique for implementing new, more efficient versions of statistical procedures. Near exact solutions are constructed for a number of concrete multi-dimensional problems: estimation of expectation vectors, regression and discriminant analysis, and for the solution to large systems of empiric linear algebraic equations. It is remarkable that these solutions prove to be not only non-degenerating and always stable, but also near exact within a wide class of populations. In the conventional situation of small dimension and large sample size these new solutions far surpass the classical, commonly used consistent ones. It can be expected in the near future, for the most part, traditional multivariate statistical software will be replaced by the always reliable and more efficient versions of statistical procedures implemented by the technology described in this book. This monograph will be of interest to a variety of specialists working with the theory of statistical methods and its applications. Mathematicians would find new classes of urgent problems to be solved in their own regions. Specialists in applied statistics creating statistical packages will be interested in more efficient methods proposed in the book. Advantages of these methods are obvious: the user is liberated from the permanent uncertainty of possible instability and inefficiency and gets algorithms with unimprovable accuracy and guaranteed for a wide class of distributions. A large community of specialists applying statistical methods to real data will find a number of always stable highly accurate versions of algorithms that will help them to better solve their scientific or economic problems. Students and postgraduates will be interested in this book as it will help them get at the foremost frontier of modern statistical science. - Presents original mathematical investigations and open a new branch of mathematical statistics - Illustrates a technique for developing always stable and efficient versions of multivariate statistical analysis for large-dimensional problems - Describes the most popular methods some near exact solutions; including algorithms of non-degenerating large-dimensional discriminant and regression analysis.

ISBN: 9786611096298

Source: 109629MIL

Nat. Bib. No.: GBA726017bnbSubjects--Topical Terms:

182818
Multivariate analysis.
Index Terms--Genre/Form:

336502
Electronic books.

LC Class. No.: QA278

Dewey Class. No.: 519.535 22

Multiparametric statistics[electronic resource] /
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300 $a 1 online resource (xvii, 315 p.)
500 $a Title from e-book title screen (viewed Jan. 16, 2008).
504 $a Includes bibliographical references and index.
505 0 $a 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 to High-Order Systems of Empirical Linear Algebraic Equations -- Appendix -- References -- Index.
520 $a This monograph presents mathematical theory of statistical models described by the essentially large number of unknown parameters, comparable with sample size but can also be much larger. In this meaning, the proposed theory can be called "essentially multiparametric". It is developed on the basis of the Kolmogorov asymptotic approach in which sample size increases along with the number of unknown parameters. This theory opens a way for solution of central problems of multivariate statistics, which up until now have not been solved. Traditional statistical methods based on the idea of an infinite sampling often break down in the solution of real problems, and, dependent on data, can be inefficient, unstable and even not applicable. In this situation, practical statisticians are forced to use various heuristic methods in the hope the will find a satisfactory solution. Mathematical theory developed in this book presents a regular technique for implementing new, more efficient versions of statistical procedures. Near exact solutions are constructed for a number of concrete multi-dimensional problems: estimation of expectation vectors, regression and discriminant analysis, and for the solution to large systems of empiric linear algebraic equations. It is remarkable that these solutions prove to be not only non-degenerating and always stable, but also near exact within a wide class of populations. In the conventional situation of small dimension and large sample size these new solutions far surpass the classical, commonly used consistent ones. It can be expected in the near future, for the most part, traditional multivariate statistical software will be replaced by the always reliable and more efficient versions of statistical procedures implemented by the technology described in this book. This monograph will be of interest to a variety of specialists working with the theory of statistical methods and its applications. Mathematicians would find new classes of urgent problems to be solved in their own regions. Specialists in applied statistics creating statistical packages will be interested in more efficient methods proposed in the book. Advantages of these methods are obvious: the user is liberated from the permanent uncertainty of possible instability and inefficiency and gets algorithms with unimprovable accuracy and guaranteed for a wide class of distributions. A large community of specialists applying statistical methods to real data will find a number of always stable highly accurate versions of algorithms that will help them to better solve their scientific or economic problems. Students and postgraduates will be interested in this book as it will help them get at the foremost frontier of modern statistical science. - Presents original mathematical investigations and open a new branch of mathematical statistics - Illustrates a technique for developing always stable and efficient versions of multivariate statistical analysis for large-dimensional problems - Describes the most popular methods some near exact solutions; including algorithms of non-degenerating large-dimensional discriminant and regression analysis.
650 0 $a Multivariate analysis. $3 182818
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