Matrix Algebra: Theory, Computations and Applications in Statistics

The book Matrix Algebra for Statistical Applications presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra. It is ideal for graduate and advanced undergraduate students, or as a supplementary text for courses in linear models or multivariate statistics.

Format: Hardback
Length: 693 pages
Publication date: 19 November 2023
Publisher: Springer International Publishing AG

This comprehensive book presents a thorough exploration of matrix algebra, a fundamental branch of mathematics with widespread applications in data science and statistical theory. It serves as a valuable resource for both students and professionals seeking to understand and apply matrix concepts in their work.

The book begins by introducing the fundamental concepts of vectors and vector spaces, laying the foundation for subsequent chapters. It then delves into the realm of basic algebraic properties of matrices, emphasizing their role in solving linear equations and performing matrix operations. The text further explores the analytic properties of vectors and matrices in multivariate calculus, providing insights into their behavior under different mathematical transformations.

In the concluding chapters, the book discusses various applications of matrix theory in statistics. It covers linear models, multivariate analysis, and stochastic processes, highlighting the importance of matrices in these areas. The book also includes discussions on the R software package, a popular tool for statistical computing. Numerous examples and exercises are provided to reinforce the concepts and facilitate a deeper understanding of matrix algebra.

Designed for graduate and advanced undergraduate students, as well as professionals seeking to expand their knowledge in matrix algebra, this book offers a self-contained description of relevant aspects of matrix algebra for applications in statistics. Its comprehensive coverage, clear explanations, and extensive examples make it an invaluable resource for anyone interested in this field.

In summary, Matrix Algebra: Theory and Applications for Statistical Analysis is a must-read for anyone seeking to excel in data science, statistical theory, or any other field that relies on matrix concepts. With its comprehensive coverage, practical insights, and emphasis on real-world applications, this book provides a solid foundation for anyone looking to advance their understanding of matrix algebra and its applications.

Dimension: 254 x 178 (mm)
ISBN-13: 9783031421433
Edition number: 3rd ed. 2024