Matrix-based introduction to multivariate data analysis - Adachi, Kohei (Singapore: Springer, 2016)
Part 1. Elementary Statistics with Matrices -- 1 Introduction to Matrix Operations -- 2 Intra-variable Statistics -- 3 Inter-variable Statistics -- Part 2. Least Squares Procedures -- 4 Regression Analysis -- 5 Principal Component Analysis (Part 1) -- 6 Principal Component Analysis 2 (Part 2) -- 7 Cluster Analysis -- Part 3. Maximum Likelihood Procedures -- 8 Maximum Likelihood and Normal Distributions -- 9 Path Analysis -- 10 Confirmatory Factor Analysis -- 11 Structural Equation Modeling -- 12 Exploratory Factor Analysis -- Part 4. Miscellaneous Procedures -- 13 Rotation Techniques -- 14 Canonical Correlation and Multiple Correspondence Analyses -- 15 Discriminant Analysis -- 16 Multidimensional Scaling -- Appendices -- A1 Geometric Understanding of Matrices and Vectors -- A2 Decomposition of Sums of Squares -- A3 Singular Value Decomposition (SVD) -- A4 Matrix Computation Using SVD -- A5 Supplements for Probability Densities and Likelihoods -- A6 Iterative Algorithms
Includes bibliography and index
Keywords: Statistics; Mathematical Statistics; Statistical Theory and Methods |; Multivariate Data Analysis