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Methods of Multivariate Analysis 2ed, by Alvin C. Rencher，John Wiley.
写的十分清晰流畅，可读性强！这是目录（有些数学符号显示不出来）
Contents 1. Introduction 1 1.1 Why Multivariate Analysis?, 1 1.2 Prerequisites, 3 1.3 Objectives, 3 1.4 Basic Types of Data and Analysis, 3 2. Matrix Algebra 5 2.1 Introduction, 5 2.2 Notation and Basic Definitions, 5 2.2.1 Matrices, Vectors, and Scalars, 5 2.2.2 Equality of Vectors and Matrices, 7 2.2.3 Transpose and Symmetric Matrices, 7 2.2.4 Special Matrices, 8 2.3 Operations, 9 2.3.1 Summation and Product Notation, 9 2.3.2 Addition of Matrices and Vectors, 10 2.3.3 Multiplication of Matrices and Vectors, 11 2.4 Partitioned Matrices, 20 2.5 Rank, 22 2.6 Inverse, 23 2.7 Positive Definite Matrices, 25 2.8 Determinants, 26 2.9 Trace, 30 2.10 Orthogonal Vectors and Matrices, 31 2.11 Eigenvalues and Eigenvectors, 32 2.11.1 Definition, 32 2.11.2 I + A and I − A, 33 2.11.3 tr(A) and |A|, 34 2.11.4 Positive Definite and Semidefinite Matrices, 34 2.11.5 The Product AB, 35 2.11.6 Symmetric Matrix, 35 v vi CONTENTS 2.11.7 Spectral Decomposition, 35 2.11.8 Square Root Matrix, 36 2.11.9 Square Matrices and Inverse Matrices, 36 2.11.10 Singular Value Decomposition, 36 3. Characterizing and Displaying Multivariate Data 43 3.1 Mean and Variance of a Univariate Random Variable, 43 3.2 Covariance and Correlation of Bivariate Random Variables, 45 3.2.1 Covariance, 45 3.2.2 Correlation, 49 3.3 Scatter Plots of Bivariate Samples, 50 3.4 Graphical Displays for Multivariate Samples, 52 3.5 Mean Vectors, 53 3.6 Covariance Matrices, 57 3.7 Correlation Matrices, 60 3.8 Mean Vectors and Covariance Matrices for Subsets of Variables, 62 3.8.1 Two Subsets, 62 3.8.2 Three or More Subsets, 64 3.9 Linear Combinations of Variables, 66 3.9.1 Sample Properties, 66 3.9.2 Population Properties, 72 3.10 Measures of Overall Variability, 73 3.11 Estimation of Missing Values, 74 3.12 Distance between Vectors, 76 4. The Multivariate Normal Distribution 82 4.1 Multivariate Normal Density Function, 82 4.1.1 Univariate Normal Density, 82 4.1.2 Multivariate Normal Density, 83 4.1.3 Generalized Population Variance, 83 4.1.4 Diversity of Applications of the Multivariate Normal, 85 4.2 Properties of Multivariate Normal Random Variables, 85 4.3 Estimation in the Multivariate Normal, 90 4.3.1 Maximum Likelihood Estimation, 90 4.3.2 Distribution of y and S, 91 4.4 Assessing Multivariate Normality, 92 4.4.1 Investigating Univariate Normality, 92 4.4.2 Investigating Multivariate Normality, 96 CONTENTS vii 4.5 Outliers, 99 4.5.1 Outliers in Univariate Samples, 100 4.5.2 Outliers in Multivariate Samples, 101 5. Tests on One or Two Mean Vectors 112 5.1 Multivariate versus Univariate Tests, 112 5.2 Tests on with Known, 113 5.2.1 Review of Univariate Test for H0: μ = μ0 with σ Known, 113 5.2.2 Multivariate Test for H0: = 0 with Known, 114 5.3 Tests on When Is Unknown, 117 5.3.1 Review of Univariate t-Test for H0: μ = μ0 with σ Unknown, 117 5.3.2 Hotelling’s T 2-Test for H0: = 0 with Unknown, 117 5.4 Comparing Two Mean Vectors, 121 5.4.1 Review of Univariate Two-Sample t-Test, 121 5.4.2 Multivariate Two-Sample T 2-Test, 122 5.4.3 Likelihood Ratio Tests, 126 5.5 Tests on Individual Variables Conditional on Rejection of H0 by the T 2-Test, 126 5.6 Computation of T 2, 130 5.6.1 Obtaining T 2 from a MANOVA Program, 130 5.6.2 Obtaining T 2 from Multiple Regression, 130 5.7 Paired Observations Test, 132 5.7.1 Univariate Case, 132 5.7.2 Multivariate Case, 134 5.8 Test for Additional Information, 136 5.9 Profile Analysis, 139 5.9.1 One-Sample Profile Analysis, 139 5.9.2 Two-Sample Profile Analysis, 141 6. Multivariate Analysis of Variance 156 6.1 One-Way Models, 156 6.1.1 Univariate One-Way Analysis of Variance (ANOVA), 156 6.1.2 Multivariate One-Way Analysis of Variance Model (MANOVA), 158 6.1.3 Wilks’ Test Statistic, 161 6.1.4 Roy’s Test, 164 6.1.5 Pillai and Lawley–Hotelling Tests, 166 viii CONTENTS 6.1.6 Unbalanced One-Way MANOVA, 168 6.1.7 Summary of the Four Tests and Relationship to T 2, 168 6.1.8 Measures of Multivariate Association, 173 6.2 Comparison of the Four Manova Test Statistics, 176 6.3 Contrasts, 178 6.3.1 Univariate Contrasts, 178 6.3.2 Multivariate Contrasts, 180 6.4 Tests on Individual Variables Following Rejection of H0 by the Overall MANOVA Test, 183 6.5 Two-Way Classification, 186 6.5.1 Review of Univariate Two-Way ANOVA, 186 6.5.2 Multivariate Two-Way MANOVA, 188 6.6 Other Models, 195 6.6.1 Higher Order Fixed Effects, 195 6.6.2 Mixed Models, 196 6.7 Checking on the Assumptions, 198 6.8 Profile Analysis, 199 6.9 Repeated Measures Designs, 204 6.9.1 Multivariate vs. Univariate Approach, 204 6.9.2 One-Sample Repeated Measures Model, 208 6.9.3 k-Sample Repeated Measures Model, 211 6.9.4 Computation of Repeated Measures Tests, 212 6.9.5 Repeated Measures with Two Within-Subjects Factors and One Between-Subjects Factor, 213 6.9.6 Repeated Measures with Two Within-Subjects Factors and Two Between-Subjects Factors, 219 6.9.7 Additional Topics, 221 6.10 Growth Curves, 221 6.10.1 Growth Curve for One Sample, 221 6.10.2 Growth Curves for Several Samples, 229 6.10.3 Additional Topics, 230 6.11 Tests on a Subvector, 231 6.11.1 Test for Additional Information, 231 6.11.2 Stepwise Selection of Variables, 233 7. Tests on Covariance Matrices 248 7.1 Introduction, 248 7.2 Testing a Specified Pattern for , 248 7.2.1 Testing H0 : = 0, 248 CONTENTS ix 7.2.2 Testing Sphericity, 250 7.2.3 Testing H0 : = σ2[(1 − ρ)I + ρJ], 252 7.3 Tests Comparing Covariance Matrices, 254 7.3.1 Univariate Tests of Equality of Variances, 254 7.3.2 Multivariate Tests of Equality of Covariance Matrices, 255 7.4 Tests of Independence, 259 7.4.1 Independence of Two Subvectors, 259 7.4.2 Independence of Several Subvectors, 261 7.4.3 Test for Independence of All Variables, 265 8. Discriminant Analysis: Description of Group Separation 270 8.1 Introduction, 270 8.2 The Discriminant Function for Two Groups, 271 8.3 Relationship between Two-Group Discriminant Analysis and Multiple Regression, 275 8.4 Discriminant Analysis for Several Groups, 277 8.4.1 Discriminant Functions, 277 8.4.2 A Measure of Association for Discriminant Functions, 282 8.5 Standardized Discriminant Functions, 282 8.6 Tests of Significance, 284 8.6.1 Tests for the Two-Group Case, 284 8.6.2 Tests for the Several-Group Case, 285 8.7 Interpretation of Discriminant Functions, 288 8.7.1 Standardized Coefficients, 289 8.7.2 Partial F-Values, 290 8.7.3 Correlations between Variables and Discriminant Functions, 291 8.7.4 Rotation, 291 8.8 Scatter Plots, 291 8.9 Stepwise Selection of Variables, 293 9. Classification Analysis: Allocation of Observations to Groups 299 9.1 Introduction, 299 9.2 Classification into Two Groups, 300 9.3 Classification into Several Groups, 304 9.3.1 Equal Population Covariance Matrices: Linear Classification Functions, 304 9.3.2 Unequal Population Covariance Matrices: Quadratic Classification Functions, 306 x CONTENTS 9.4 Estimating Misclassification Rates, 307 9.5 Improved Estimates of Error Rates, 309 9.5.1 Partitioning the Sample, 310 9.5.2 Holdout Method, 310 9.6 Subset Selection, 311 9.7 Nonparametric Procedures, 314 9.7.1 Multinomial Data, 314 9.7.2 Classification Based on Density Estimators, 315 9.7.3 Nearest Neighbor Classification Rule, 318 10. Multivariate Regression 322 10.1 Introduction, 322 10.2 Multiple Regression: Fixed x’s, 323 10.2.1 Model for Fixed x’s, 323 10.2.2 Least Squares Estimation in the Fixed-x Model, 324 10.2.3 An Estimator for σ2, 326 10.2.4 The Model Corrected for Means, 327 10.2.5 Hypothesis Tests, 329 10.2.6 R2 in Fixed-x Regression, 332 10.2.7 Subset Selection, 333 10.3 Multiple Regression: Random x’s, 337 10.4 Multivariate Multiple Regression: Estimation, 337 10.4.1 The Multivariate Linear Model, 337 10.4.2 Least Squares Estimation in the Multivariate Model, 339 10.4.3 Properties of Least Squares Estimators Bˆ , 341 10.4.4 An Estimator for , 342 10.4.5 Model Corrected for Means, 342 10.5 Multivariate Multiple Regression: Hypothesis Tests, 343 10.5.1 Test of Overall Regression, 343 10.5.2 Test on a Subset of the x’s, 347 10.6 Measures of Association between the y’s and the x’s, 349 10.7 Subset Selection, 351 10.7.1 Stepwise Procedures, 351 10.7.2 All Possible Subsets, 355 10.8 Multivariate Regression: Random x’s, 358 11. Canonical Correlation 361 11.1 Introduction, 361 11.2 Canonical Correlations and Canonical Variates, 361 CONTENTS xi 11.3 Properties of Canonical Correlations, 366 11.4 Tests of Significance, 367 11.4.1 Tests of No Relationship between the y’s and the x’s, 367 11.4.2 Test of Significance of Succeeding Canonical Correlations after the First, 369 11.5 Interpretation, 371 11.5.1 Standardized Coefficients, 371 11.5.2 Correlations between Variables and Canonical Variates, 373 11.5.3 Rotation, 373 11.5.4 Redundancy Analysis, 373 11.6 Relationships of Canonical Correlation Analysis to Other Multivariate Techniques, 374 11.6.1 Regression, 374 11.6.2 MANOVA and Discriminant Analysis, 376 12. Principal Component Analysis 380 12.1 Introduction, 380 12.2 Geometric and Algebraic Bases of Principal Components, 381 12.2.1 Geometric Approach, 381 12.2.2 Algebraic Approach, 385 12.3 Principal Components and Perpendicular Regression, 387 12.4 Plotting of Principal Components, 389 12.5 Principal Components from the Correlation Matrix, 393 12.6 Deciding How Many Components to Retain, 397 12.7 Information in the Last Few Principal Components, 401 12.8 Interpretation of Principal Components, 401 12.8.1 Special Patterns in S or R, 402 12.8.2 Rotation, 403 12.8.3 Correlations between Variables and Principal Components, 403 12.9 Selection of Variables, 404 13. Factor Analysis 408 13.1 Introduction, 408 13.2 Orthogonal Factor Model, 409 13.2.1 Model Definition and Assumptions, 409 13.2.2 Nonuniqueness of Factor Loadings, 414 13.3 Estimation of Loadings and Communalities, 415 13.3.1 Principal Component Method, 415 13.3.2 Principal Factor Method, 421 xii CONTENTS 13.3.3 Iterated Principal Factor Method, 424 13.3.4 Maximum Likelihood Method, 425 13.4 Choosing the Number of Factors, m, 426 13.5 Rotation, 430 13.5.1 Introduction, 430 13.5.2 Orthogonal Rotation, 431 13.5.3 Oblique Rotation, 435 13.5.4 Interpretation, 438 13.6 Factor Scores, 438 13.7 Validity of the Factor Analysis Model, 443 13.8 The Relationship of Factor Analysis to Principal Component Analysis, 447 14. Cluster Analysis 451 14.1 Introduction, 451 14.2 Measures of Similarity or Dissimilarity, 452 14.3 Hierarchical Clustering, 455 14.3.1 Introduction, 455 14.3.2 Single Linkage (Nearest Neighbor), 456 14.3.3 Complete Linkage (Farthest Neighbor), 459 14.3.4 Average Linkage, 463 14.3.5 Centroid, 463 14.3.6 Median, 466 14.3.7 Ward’s Method, 466 14.3.8 Flexible Beta Method, 468 14.3.9 Properties of Hierarchical Methods, 471 14.3.10 Divisive Methods, 479 14.4 Nonhierarchical Methods, 481 14.4.1 Partitioning, 481 14.4.2 Other Methods, 490 14.5 Choosing the Number of Clusters, 494 14.6 Cluster Validity, 496 14.7 Clustering Variables, 497 15. Graphical Procedures 504 15.1 Multidimensional Scaling, 504 15.1.1 Introduction, 504 15.1.2 Metric Multidimensional Scaling, 505 15.1.3 Nonmetric Multidimensional Scaling, 508 CONTENTS xiii 15.2 Correspondence Analysis, 514 15.2.1 Introduction, 514 15.2.2 Row and Column Profiles, 515 15.2.3 Testing Independence, 519 15.2.4 Coordinates for Plotting Row and Column Profiles, 521 15.2.5 Multiple Correspondence Analysis, 526 15.3 Biplots, 531 15.3.1 Introduction, 531 15.3.2 Principal Component Plots, 531 15.3.3 Singular Value Decomposition Plots, 532 15.3.4 Coordinates, 533 15.3.5 Other Methods, 535 A. Tables 549 B. Answers and Hints to Problems 591 C. Data Sets and SAS Files 679 References 681
PDF格式！附件Frontmatter是详细目录 （初次发帖，多多指教啊！）
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[此贴子已经被作者于2005-11-3 23:44:04编辑过]
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