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AN INTRODUCTION TO MULTIVARIATE STATISTICAL ANALYSIS PDF

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An Introduction to Multivariate Statistical Analysis Third EditionT. W. ANDERSON Stanford University Views 17MB Size Report. DOWNLOAD PDF. An Introduction to Multivariate Statistical Analysis (Wiley Series in Probability and Statistics) - 3rd edition. Home · An Introduction to Multivariate Statistical. An Introduction to Multivariate Statistical myavr.info - Ebook download as PDF File .pdf), Text File .txt) or read book online.


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Multivariate Statistical Analysis: Selected Lecture Notes, Radoslav Harman. 1 Principal Components Analysis. Mathematical background. An Introduction to Multivariate. Statistical Analysis. Second Edition. T. W. ANDERSON. Professor of Statistics and Economics. Stanford University. JOHN WILEY. Request PDF on ResearchGate | An Introduction to Multivariate Statistics | The more commonly known statistical procedures, such as the t-test, analysis of.

Wiley Eastern Ltd. The lattice structure of orthogonal linear models and orthogonal variance component models. Distribution of eigenvalues in multivariate statistical analysis. Multivariate Analysis Symmetry and lattice conditional independence in a multivariate normal distribution. Lattice-ordered conditional independence models for missing data. Statistics and Probability Letters 12 Lattice models for conditional independence in a multivariate normal distribution.

Normal linear models with lattice conditional independence restrictions. In Multivariate Analysis and its Applications T. Anderson, K. Fang, I. Normal linear regression models with recursive graphical Markov structure. Multivariate Analysis 66 Wishart distributions on homogeneous cones. Shrinkage estimators for covariance matrices. Biometrics 57 Das Gupta, S. Monotonicity of the power functions of some tests of the multivariate linear hpothesis.

Drton, M.

Applied Multivariate Statistical Analysis∗

Lattice conditional independence models for seemingly unrelated regression models with missing data. The Characteristic Function; Moments, 41 2. Theoretical Properties of Estimators of the Mean Vector, 83 3. Improved Estimation of the Mean, 91 3. Currelation CoclTiciellt or a 13ivariate Sample, 4. The MUltiple Correlation Codficient, 4. Uses of the T"-Statistic, 5.

Some Optimal Properties or the T 1 -Test, 5. The Problem of Classification, 6. Pro ;eOureJ.

Probabilities of Misc1assification, 6. Classification into One of Several Populations, 6. Introduction, 7. The Wishart Distribution, 7.

Applied Multivariate Statistical Analysis

Some Properties of the Wishart Distribution, 7. Cochran's Theorem, 7.

The Generalized Variance, 7. Improved Estimation of the Covariance Matrix, 7. Introduction, 8. Estimators of Parameters in Multivariate Linear Regl'ession, 8. Other Criteria for Testing the Linear Hypothesis, 8. Multivariate Analysis of Variance, 8.

Some Optimal Properties of Tests, 8. I ntroductiom, 9.

Other Criteria, 9. Step-Down Procedures, 9. An Example, 9. The Case of Two Sets of Variates, 9.

Introduction, Asymptotic EXpansions of the Distributions of the Criteria, The Case of Two Populations, Admissibility of Tests, Definition of Principal Components in the Populat 1 0n, An Example, Statistical Inference, Canonical Correlations and Variates in the Population, Estimation of Canonical Correlations and Variates, Reduced Rank Regression, The Case of Two Wishart Matrices, Canonical Correlations, Asymptotic Distribution in a Regression Model, The Model, Estimation for Fixed Factors, Factor Interpretation and Transformation, Estimation for Identification by Specified Zeros, Undirected Graphs, Directed Graphs, Chain Graphs, Definition of a Matrix and Operations on Matrices, A.

Partitioned Vectors and Matrices, A.

Some Miscellaneous Results, A. Wilks' Likelihood Criterion: Factors C p, m, M to Adjust to X;. Tables of Significance Points for the. Since the second edition was published, multivariate analysis has been developed and extended in many directions.

Rather than attempting to cover, or even survey, the enlarged scope, I have elected to elucidate several aspects that are particularly interesting and useful for methodology and comprehen- sion. Earlier editions included some methods that could be carried out on an adding machine! In the twenty-first century, however, computational tech- niques have become so highly developed and improvements come so rapidly that it is impossible to include all of the relevant methods in a volume on the general mathematical theory.

Some aspects of statistics exploit computational power such as the resampling technologies; these are not covered here. The definition of multivariate statistics implies the treatment of variables that are interrelated. Several chapters are devoted to measures of correlation and tests of independence.

A new chapter, "Patterns of Dependence; Graph- ical Models" has been added. A so-called graphical model is a set of vertices Or nodes identifying observed variables together with a new set of edges suggesting dependences between variables. The algebra of such graphs is an outgrowth and development of path analysis and the study of causal chains.

A graph may represent a sequence in time or logic and may suggest causation of one set of variables by another set. Another new topic systematically presented in the third edition is that of elliptically contoured distributions.

The multivariate normal distribution, which is characterized by the mean vector and covariance matrix, has a limitation that the fourth-order moments of the variables are determined by the first- and second-order moments. The class.

A density in this class has contours of equal density which are ellipsoids as does a normal density, but the set of fourth-order moments has one further degree of freedom. This topic is expounded by the addition of sections to appropriate chapters.

Reduced rank regression developed in Chapters 12 and 13 provides a method of reducing the number of regression coefficients to be estimated in the regression of one set of variables to another. This approach includes the limited-information maximum-likelihood estimator of an equation in a simul- taneous equations model. The preparation of the third edition has been benefited by advice and comments of readers of the first and second editions as well as by reviewers of the current revision.

In addition to readers of the earlier editions listed in those prefaces I want to thank Michael Perlman and Kathy Richards for their assistance in getting this manuscript ready. Stanford, California February T. Canonical Correlations and Variates in the Population, Estimation of Canonical Correlations and Variates, Reduced Rank Regression, The Case of Two Wishart Matrices, Canonical Correlations, Asymptotic Distribution in a Regression Model, The Model, Estimation for Fixed Factors, Factor Interpretation and Transformation, Estimation for Identification by Specified Zeros, Undirected Graphs, Directed Graphs, Chain Graphs, Definition of a Matrix and Operations on Matrices, A.

Partitioned Vectors and Matrices, A. Some Miscellaneous Results, A. Wilks' Likelihood Criterion: Factors C p, m, M to Adjust to X;. Tables of Significance Points for the. Since the second edition was published, multivariate analysis has been developed and extended in many directions.

An Introduction to Multivariate Statistical Analysis, 3rd Edition

Rather than attempting to cover, or even survey, the enlarged scope, I have elected to elucidate several aspects that are particularly interesting and useful for methodology and comprehen- sion. Earlier editions included some methods that could be carried out on an adding machine! In the twenty-first century, however, computational tech- niques have become so highly developed and improvements come so rapidly that it is impossible to include all of the relevant methods in a volume on the general mathematical theory.

Some aspects of statistics exploit computational power such as the resampling technologies; these are not covered here. The definition of multivariate statistics implies the treatment of variables that are interrelated. Several chapters are devoted to measures of correlation and tests of independence. A new chapter, "Patterns of Dependence; Graph- ical Models" has been added. A so-called graphical model is a set of vertices Or nodes identifying observed variables together with a new set of edges suggesting dependences between variables.

The algebra of such graphs is an outgrowth and development of path analysis and the study of causal chains. A graph may represent a sequence in time or logic and may suggest causation of one set of variables by another set.

Another new topic systematically presented in the third edition is that of elliptically contoured distributions. The multivariate normal distribution, which is characterized by the mean vector and covariance matrix, has a limitation that the fourth-order moments of the variables are determined by the first- and second-order moments.

The class. A density in this class has contours of equal density which are ellipsoids as does a normal density, but the set of fourth-order moments has one further degree of freedom. This topic is expounded by the addition of sections to appropriate chapters. Reduced rank regression developed in Chapters 12 and 13 provides a method of reducing the number of regression coefficients to be estimated in the regression of one set of variables to another.

This approach includes the limited-information maximum-likelihood estimator of an equation in a simul- taneous equations model. The preparation of the third edition has been benefited by advice and comments of readers of the first and second editions as well as by reviewers of the current revision.

About the Author

In addition to readers of the earlier editions listed in those prefaces I want to thank Michael Perlman and Kathy Richards for their assistance in getting this manuscript ready. Stanford, California February T.

This new edition purports to bring the original edition up to date by substantial revision, rewriting, and additions. The basic approach has been maintained, llamely, a mathematically rigorous development of statistical methods for observations consisting of several measurements or characteristics of each sUbject and a study of their properties.

The general outline of topics has been retained. The method of maximum likelihood has been augmented by other consid- erations. In point estimation of the mf"an vectOr and covariance matrix alternatives to the maximum likelihood estimators that are better with respect to certain loss functions, such as Stein and Bayes estimators, have been introduced. In testing hypotheses likelihood ratio tests have been supplemented by other invariant procedures. New results on distributions and asymptotic distributions are given; some significant points are tabulated.

Properties of these procedures, such as power functions, admissibility, unbi- asedness, and monotonicity of power functions, are studied. Simultaneous confidence intervals for means and covariances are developed. A chapter on factor analysis replaces the chapter sketching miscellaneous results in the first edition. Some new topics, including simultaneous equations models and linear functional relationships, are introduced. Additional problems present further results. FOr a comprehensive listing of papers until and books until the reader is referred to A Bibliography of Multivariate Statistical Analysis by Anderson, Das Gupta, and Styan Further references can be found in Multivariate Analysis: I am in debt to many students, colleagues, and friends for their suggestions and assistance; they include Yasuo Amemiya, James Berger, Byoung-Seon Choi.

Special thanks go to Johanne Thiffault and George P. H, Styan for their precise attention. Seven tables of significance points are given in Appendix B to facilitate carrying out test procedures. Tables 1, 5, and 7 are Tables 47, 50, and 53, respectively, of Biometrika Tables for Statisticians, Vol. Pearson and H. Table 2 is made up from three tables prepared by A.

Simulation and Computa- tion Tables 3 and 4 are Tables 6. Nagarscnkcr and K. Pillai, Aerospacc Research Laboratorics The author is indebted to the authors and publishers listed above for permission to reproduce these tables. California June T.

It is hoped that the book will also serve as an introduction to many topics in this area to statisticians who are not students and will be used as a reference by other statisticians. For several years the book in the form of dittoed notes has been used in a two-semester sequence of graduate courses at Columbia University; the first six chapters constituted the text for the first semester, emphasizing correla- tion theory.

It is assumed that the reader is familiar with the usual theory of univariate statistics, particularly methods based on the univariate normal distribution. A knowledge of matrix algebra is also a prerequisite; however, an appendix on this topic has been included. It is hoped that the more basic and important topics are treated here, though to some extent the coverage is a matter of taste. Some 0f the mOre recent and advanced developments are only briefly touched on in the late chapter.

The method of maximum likelihood is used to a large extent. This leads to reasonable procedures; in some cases it can be proved that they are optimal.Some aspects of statistics exploit computational power such as the resampling technologies; these are not covered here.

When the individual is drawn ran- domly, we consider the vector as a random vector with a distribution or probability law describing that population.

We think of the entire vector as an observation from a multivariate population or distribution. First consider the case of two real random variables t X and Y. We think of the entire vector as an observation from a multivariate population or distribution.

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