Matrix-Based Introduction to Multivariate Data Analysis

• Κύριος συγγραφέας: Adachi, Kohei (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2016.
• Θέματα: Statistics.; Mathematical statistics.; Statistical Theory and Methods.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.; Statistics and Computing/Statistics Programs.; Statistics for Business/Economics/Mathematical Finance/Insurance.; Probability and Statistics in Computer Science.

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...

Πίνακας περιεχομένων:

• 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.