An Introduction To Multivariate Statistical Analysis, Third Edition

基本信息

Hardcover: 752 pages

Publisher: John Wiley & Sons Inc; 3Rev Ed edition (8 Aug. 2003)

Language: English

ISBN-10: 0471360910

ISBN-13: 978-0471360919

Product Dimensions: 166 x 246 x 40mm | 1,128g

页面参数仅供参考，具体以实物为准

书籍简介

本书是世界知名统计学家的力作，主要内容有多元正态分布、方差分析、回归分析、因子分析、椭球等高分布、相依性模式、图模型。附录中还列出了矩阵理论、Wilk似然准则和其他常用检验的显著性水平的分位数。

本书在世界各高等学校中广为采用，是一本经典的多元统计分析课程的教材，也可供相关统计研究人员、应用多元统计的科技工作者参考。

Perfected over three editions and more than forty years, this field- and classroom-tested reference:

* Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures.

* Treats all the basic and important topics in multivariate statistics.

* Adds two new chapters, along with a number of new sections.

* Provides the most methodical, up-to-date information on MV statistics available.

目录

Preface to the Third Edition.

Preface to the Second Edition.

Preface to the First Edition.

1. Introduction.

2. The Multivariate Normal Distribution.

3. Estimation of the Mean Vector and the Covariance Matrix.

4. The Distributions and Uses of Sample Correlation Coefficients.

5. The Generalized T2-Statistic.

6. Classification of Observations.

7. The Distribution of the Sample Covariance Matrix and the Sample Generalized Variance.

8. Testing the General Linear Hypothesis: Multivariate Analysis of Variance

9. Testing Independence of Sets of Variates.

10. Testing Hypotheses of Equality of Covariance Matrices and Equality of Mean Vectors and Covariance Matrices.

11. Principal Components.

12. Cononical Correlations and Cononical Variables.

13. The Distributions of Characteristic Roots and Vectors.

14. Factor Analysis.

15. Pattern of Dependence; Graphical Models.

Appendix A: Matrix Theory.

Appendix B: Tables.

References.

Index.

作者简介

T. W. Anderson 1918年6月5日出生于美国明尼阿波利斯市，1945年获普林斯顿大学数学专业博士学位，后任教于芝加哥大学、哥伦比亚大学及斯坦福大学。美国科学院院士，数理统计学会、统计学会、经济协会、艺术与科学学会会士。Anderson教授一生获得过许多荣誉，且著述颇丰，在统计领域做出了卓越的贡献。

THEODORE W. ANDERSON, Professor Emeritus of Statistics and Economics at Stanford University, earned his PhD in mathematics at Princeton University. He is the author of The Statistical Analysis of Time Series, published by Wiley, as well as The New Statistical Analysis of Data and A Bibliography of Multivariate Statistical Analysis. Anderson is a member of the National Academy of Sciences and a Fellow of the Institute of Mathematical Statistics, the American Statistical Association, the Econometric Society, and the American Academy of Arts and Sciences.