Matrix-Based Introduction to Multivariate Data Analysis
This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The...
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| Language: | English |
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Singapore :
Springer Singapore : Imprint: Springer,
2016.
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| Edition: | 1st ed. 2016. |
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| Online Access: | https://doi.org/10.1007/978-981-10-2341-5 |
Table of Contents:
- 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
- References
- Index.