Härdle W.K., Simar L. Applied Multivariate Statistical Analysis

5th edition. — New York: Springer, 2019. — 550 p.This textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields, and all the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. For this new edition, the book has been updated and extensively revised and now includes an extended chapter on cluster analysis. All solutions to the exercises are supplemented by R and MatLAB or SAS computer code and can be downloaded from the Quantlet platform. Practical exercises from this book and their solutions can also be found in the accompanying Springer book by W.K. Härdle and Z. Hlávka: Multivariate Statistics - Exercises and Solutions. The Quantlet platform, quantlet.de, quantlet.com, quantlet.org, is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding data-driven document-based visualization allow readers to reproduce the tables, pictures and calculations presented in this Springer book.Contents: Descriptive Techniques
Comparison of Batches
Boxplots
Histograms
Kernel Densities
Scatterplots
Chernoff-Flury Faces
Andrews' Curves
Parallel Coordinate Plots
Hexagon Plots
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ExercisesMultivariate Random Variables
Elementary Operations
Spectral Decompositions
Quadratic Forms
Derivatives
Partitioned Matrices
Geometrical Aspects
Exercises
Moving to Higher Dimensions
Covariance
Correlation
Summary Statistics
Linear Model for Two Variables
Simple Analysis of Variance
Multiple Linear Model
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ExercisesMultivariate Distributions
Distribution and Density Function
Moments and Characteristic Functions
Transformations
The Multinormal Distribution
Sampling Distributions and Limit Theorems
Heavy-Tailed Distributions
Copulae
Bootstrap
ExercisesElementary Properties of the Multinormal
The Wishart Distribution
Hotelling's T-Distribution
Spherical and Elliptical Distributions
ExercisesTheory of Estimation
The Likelihood Function
The Cramer–Rao Lower Bound
ExercisesHypothesis Testing
Likelihood Ratio Test
Linear Hypothesis
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ExercisesMultivariate Techniques
Regression Models
ANOVA Models
ANCOVA Models
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Multinomial Sampling and Contingency Tables
Log-Linear Models for Contingency Tables
Testing Issues with Count Data
Logit Models
ExercisesVariable Selection
Lasso in the Linear Regression Model
Lasso in High Dimensions
Lasso in Logit Model
Elastic Net
Elastic Net in Linear Regression Model
Elastic Net in Logit Model
Group Lasso
ExercisesDecomposition 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
Exercises
Principal Components Analysis
Standardized Linear Combination
Principal Components in Practice
Interpretation of the PCs
Asymptotic Properties of the PCs
Normalized Principal Components Analysis
Principal Components as a Factorial Method
Common Principal Components
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More Examples
ExercisesFactor Analysis
The Orthogonal Factor Model
Estimation of the Factor Model
Factor Scores and Strategies
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ExercisesCluster Analysis
The Problem
The Proximity Between Objects
Cluster Algorithms
Adaptive Weights Clustering
Spectral Clustering
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ExercisesAllocation Rules for Known Distributions
Discrimination Rules in Practice
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ExercisesMotivation
Chi-Square Decomposition
Correspondence Analysis in Practice
Exercises
Most Interesting Linear Combination
Canonical Correlation in Practice
ExercisesThe Problem
Metric Multidimensional Scaling
Nonmetric Multidimensional Scaling
ExercisesDesign of Data Generation
Estimation of Preference Orderings
ExercisesPortfolio Choice
Efficient Portfolio
Efficient Portfolios in Practice
The Capital Asset Pricing Model (CAPM)Computationally Intensive Techniques
Simplicial Depth
Projection Pursuit
Sliced Inverse Regression
Support Vector Machines
Classification and Regression Trees
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ExercisesMathematical Abbreviations
Moments
Distributions
Swiss Bank Notes
Classic Blue Pullovers Data
US Crime Data
Bankruptcy Data I
Timebudget Data
Vocabulary Data