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Author: James E. Gentle
Publisher: Springer Science & Business Media
ISBN: 0387708723
Category : Computers
Languages : en
Pages : 536
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Book Description
Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.
Author: James E. Gentle
Publisher: Springer Science & Business Media
ISBN: 0387708723
Category : Computers
Languages : en
Pages : 536
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Book Description
Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.
Author: Nathaniel Johnston
Publisher: Springer Nature
ISBN: 3030528111
Category : Mathematics
Languages : en
Pages : 482
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Book Description
This textbook emphasizes the interplay between algebra and geometry to motivate the study of linear algebra. Matrices and linear transformations are presented as two sides of the same coin, with their connection motivating inquiry throughout the book. By focusing on this interface, the author offers a conceptual appreciation of the mathematics that is at the heart of further theory and applications. Those continuing to a second course in linear algebra will appreciate the companion volume Advanced Linear and Matrix Algebra. Starting with an introduction to vectors, matrices, and linear transformations, the book focuses on building a geometric intuition of what these tools represent. Linear systems offer a powerful application of the ideas seen so far, and lead onto the introduction of subspaces, linear independence, bases, and rank. Investigation then focuses on the algebraic properties of matrices that illuminate the geometry of the linear transformations that they represent. Determinants, eigenvalues, and eigenvectors all benefit from this geometric viewpoint. Throughout, “Extra Topic” sections augment the core content with a wide range of ideas and applications, from linear programming, to power iteration and linear recurrence relations. Exercises of all levels accompany each section, including many designed to be tackled using computer software. Introduction to Linear and Matrix Algebra is ideal for an introductory proof-based linear algebra course. The engaging color presentation and frequent marginal notes showcase the author’s visual approach. Students are assumed to have completed one or two university-level mathematics courses, though calculus is not an explicit requirement. Instructors will appreciate the ample opportunities to choose topics that align with the needs of each classroom, and the online homework sets that are available through WeBWorK.
Author: Karim M. Abadir
Publisher: Cambridge University Press
ISBN: 9780521822893
Category : Business & Economics
Languages : en
Pages : 472
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Book Description
Matrix Algebra is the first volume of the Econometric Exercises Series. It contains exercises relating to course material in matrix algebra that students are expected to know while enrolled in an (advanced) undergraduate or a postgraduate course in econometrics or statistics. The book contains a comprehensive collection of exercises, all with full answers. But the book is not just a collection of exercises; in fact, it is a textbook, though one that is organized in a completely different manner than the usual textbook. The volume can be used either as a self-contained course in matrix algebra or as a supplementary text.
Author: Stephen Boyd
Publisher: Cambridge University Press
ISBN: 1316518965
Category : Business & Economics
Languages : en
Pages : 477
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Book Description
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
Author: A R G Heesterman
Publisher: World Scientific Publishing Company
ISBN: 9813103639
Category : Business & Economics
Languages : en
Pages : 476
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Book Description
This textbook addresses itself to two groups of students who need mathematics in an applied context: undergraduates starting at the beginning, and postgraduates who need reference-material, but who, not being mathematics specialists, nevertheless are not best served by an ordinary mathematics textbook, which will generally be at a higher level of abstraction. It gives full proofs throughout, and is illustrated with a large number of numerical examples, reinforcing the student's grasp of the topics covered by exercises and corresponding answersheets, and by the corresponding tutorial program ILLUSTRATE. The program ‘Illustrate’ will run on any IBM compatible micro-computer. The relevant areas of application are economics, econometrics, mathematical programming and engineering.
Author: BISWAS, SUDDHENDU
Publisher: PHI Learning Pvt. Ltd.
ISBN: 8120346238
Category : Mathematics
Languages : en
Pages : 454
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Book Description
Intended as a text for postgraduate and undergraduate honours students of Statistics, Mathematics, Operations Research as well as students in various branches of Engineering, this student-friendly book gives an indepth analysis of Matrix Algebra and all the major topics related to it. Divided into 12 chapters, the book begins with a discussion on Elements of Matrix Theory and Some Special Matrices. Then it goes on to give a detailed discussion on Scalar Function and Inverse of a Matrix, Rank of a Matrix, Generalized Inverse of a Matrix, and Quadric Forms and Inequalities. The book concludes by giving Some Applications of Algebra of Matrices, Matrices in the Infinite Dimensional Vector Space, and Computational Tracts in Matrices. KEY FEATURES Gives a large number of both solved and unsolved problems of Elementary Matrix. Provides an exhaustive treatment of Generalized Inverse Matrix with many applications in Statistics. Devotes one chapter exclusively to application of Matrices. Provides one full chapter on Matrices in the Infinite Dimensional Vector Space, which will be quite useful for postgraduate students. Gives an Appendix on R Software which will be extremely useful for students of Statistics. Provides Question Bank which will greatly benefit both undergraduate and postgraduate students. This book, which beautifully blends both theory and applications of Matrix Algebra, should prove to be an invaluable text for the students.
Author: Philip J. Davis
Publisher: John Wiley & Sons
ISBN:
Category : Mathematics
Languages : en
Pages : 376
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Book Description
Author: Autar Kaw
Publisher: Lulu.com
ISBN: 0615251269
Category : Education
Languages : en
Pages : 185
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Book Description
Since 2002, the Introduction to Matrix Algebra book has been downloaded by more than 30,000 users from 50 different countries. This book is an extended primer for undergraduate Matrix Algebra. The book is either to be used as a refresher material for students who have already taken a course in Matrix Algebra or used as a just-in-time tool if the burden of teaching Matrix Algebra has been placed on several courses. In my own department, the Linear Algebra course was taken out of the curriculum a decade ago. It is now taught just in time in courses like Statics, Programming Concepts, Vibrations, and Controls. There are ten chapters in the book 1) INTRODUCTION, 2) VECTORS, 3) BINARY MATRIX OPERATIONS, 4) UNARY MATRIX OPERATIONS, 5) SYSTEM OF EQUATIONS, 6) GAUSSIAN ELIMINATION, 7) LU DECOMPOSITION, 8) GAUSS-SEIDAL METHOD, 9) ADEQUACY OF SOLUTIONS, 10) EIGENVALUES AND EIGENVECTORS.
Author: Ivan Savov
Publisher:
ISBN: 9780992001025
Category : MATHEMATICS
Languages : en
Pages : 596
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Book Description
This textbook covers the material for an undergraduate linear algebra course: vectors, matrices, linear transformations, computational techniques, geometric constructions, and theoretical foundations. The explanations are given in an informal conversational tone. The book also contains 100+ problems and exercises with answers and solutions. A special feature of this textbook is the prerequisites chapter that covers topics from high school math, which are necessary for learning linear algebra. The presence of this chapter makes the book suitable for beginners and the general audience-readers need not be math experts to read this book. Another unique aspect of the book are the applications chapters (Ch 7, 8, and 9) that discuss applications of linear algebra to engineering, computer science, economics, chemistry, machine learning, and even quantum mechanics.
Author: Marvin H. J. Gruber
Publisher: John Wiley & Sons
ISBN: 1118592557
Category : Mathematics
Languages : en
Pages : 391
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Book Description
A self-contained introduction to matrix analysis theory and applications in the field of statistics Comprehensive in scope, Matrix Algebra for Linear Models offers a succinct summary of matrix theory and its related applications to statistics, especially linear models. The book provides a unified presentation of the mathematical properties and statistical applications of matrices in order to define and manipulate data. Written for theoretical and applied statisticians, the book utilizes multiple numerical examples to illustrate key ideas, methods, and techniques crucial to understanding matrix algebra’s application in linear models. Matrix Algebra for Linear Models expertly balances concepts and methods allowing for a side-by-side presentation of matrix theory and its linear model applications. Including concise summaries on each topic, the book also features: Methods of deriving results from the properties of eigenvalues and the singular value decomposition Solutions to matrix optimization problems for obtaining more efficient biased estimators for parameters in linear regression models A section on the generalized singular value decomposition Multiple chapter exercises with selected answers to enhance understanding of the presented material Matrix Algebra for Linear Models is an ideal textbook for advanced undergraduate and graduate-level courses on statistics, matrices, and linear algebra. The book is also an excellent reference for statisticians, engineers, economists, and readers interested in the linear statistical model.
