Applied multivariate statistical analysis / Wolfgang Härdle, Léopold Simar.

Includes bibliographical references.

I. Descriptive Techniques: -- 1. Comparison of Batches -- Boxplots -- Histograms -- Kernel Densities -- Scatterplots -- Chernoff-Flury Faces -- Andrews' Curves -- Parallel Coordinate Plots -- Boston Housing -- Multivariate Random Variables: -- 2. A Short Excursion into Matrix Algebra -- Elementary Operations -- Spectral Decompositions -- Quadratic Forms -- Derivatives -- Partitioned Matrices -- Geometrical Aspects -- 3. Moving to Higher Dimensions -- Covariance -- Correlation -- Summary Statistics -- Linear Model for Two Variables -- Simple Analysis of Variance -- Multiple Linear Model -- Boston Housing -- 4. Multivariate Distributions -- Distribution and Density Function -- Moments and Characteristic Functions -- Transformations -- The Multinomial Distribution -- Sampling Distributions and Limit Theorems -- Heavy-Tailed Distributions -- Copulae -- Bootstrap -- 5. Theory of the Multinormal -- Elementary Properties of the Multmormal -- The Wishart Distribution -- Hotelling's T2-Distribution -- Spherical and Elliptical Distributions -- 6. Theory of Estimation -- The Likelihood Function -- Tim Cramer-Rao Lower Bound -- 7. Hypothesis Testing -- Likelihood Ratio Test -- Linear Hypothesis -- Boston Housing -- II. Multivariate Techniques: -- 8. Decomposition of Data Matrices by Factors -- The Geometric Point of View -- Fitting the p-dimensional Point Cloud -- Fitting the n-dimensional Point Cloud -- Relations between Subspaces -- Practical Computation -- 9. Principal Components Analysis -- Standardized Linear Combination -- Principal Component Practice -- Interpretation of the PCs -- Asymptotic Properties of the: PCs -- Normalized Principal Components Analysis -- Principal Components us a Factorial Method -- Common Principal Components -- Boston Housing -- More Examples -- 10. Factor Analysis -- The Orthogonal Factor Model -- Estimation of the Factor Model -- Factor Scores and Strategies -- Boston Housing -- 11. Cluster Analysis -- The Problem -- The Proximity between Objects -- Cluster Algorithms -- Boston Housing -- 12. Discriminant Analysis -- Allocation Rules for Known Distributions -- Discrimination Rules in Practice -- Boston Housing -- 13. Correspondence Analysis -- Motivation -- Chi-square Decomposition -- Correspondence Analysis in Practice -- 14. Canonical Correlation Analysis -- Most Interesting Linear Combination -- Canonical Correlation in Practice -- 15. Multidimensional Scaling -- The Problem -- Metric Multidimensional Scaling -- Nonmetric Multidimensional Scaling -- 16. Conjoint Measurement Analysis -- Introduction -- Design of Data Generation -- Estimation of Preference Ordering -- 17. Applications in Finance -- Portfolio Choice -- Efficient Portfolio -- Efficient Portfolios in Practice -- The Capital Pricing Model (CAPM) -- 18. Computationally Intensive Techniques -- Simplicial Depth -- Projection Pursuit -- Sliced Inverse Regression -- Support. Vector Machines -- Classification and Regression Trees -- Boston Housing -- V. Appendix : -- A. Symbols and Notations -- B. Data -- Boston Housing Data -- Swiss Bank Notes -- Car Data -- Classic Blue Pullovers Data -- U.S. Companies Data -- French Food Data -- Car Marks -- French Baccalauréat Frequencies -- Journatux Data -- U.S. Crime Data -- Plasma Data -- WAIS Data -- ANOVA Data -- Timebudget Data -- Geopol Data -- U.S. Health Data -- Vocabulary Data -- Athletic Records Data -- Unemployment Data -- Annual Population Data -- Bankruptcy Data.

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