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Book Description
Matrix Differential Calculus With Applications in Statistics and Econometrics Revised Edition Jan R. Magnus， CentER， Tilburg University， The Netherlands and Heinz Neudecker， Cesaro， Schagen， The Netherlands ? .deals rigorously with many of the problems that have bedevilled the subject up to the present time.? - Stephen Pollock， Econometric Theory ?I continued to be pleasantly surprised by the variety and usefulness of its contents ? - Isabella Verdinelli， Journal of the American Statistical Association Continuing the success of their first edition， Magnus and Neudecker present an exhaustive and self-contained revised text on matrix theory and matrix differential calculus. Matrix calculus has become an essential tool for quantitative methods in a large number of applications， ranging from social and behavioural sciences to econometrics. While the structure and successful elements of the first edition remain， this revised and updated edition contains many new examples and exercises.
* Contains the essentials of multivariable calculus with an emphasis on the use of differentials
* Many new examples and exercises
* Fulfils the need for a unified and self-contained treatment of matrix differential calculus
* Includes new developments in this field
Part I presents a concise， yet thorough overview of matrix algebra， while the second part develops the theory of differentials. The remaining Parts III to VI combine the theory and application of matrix differential calculus providing the practitioner and researcher with both a quick review and a detailed reference.

The publisher， John Wiley & Sons
This book provides a unified treatment of matrix differential calculus， specifically written for econometricians and statisticians. Divided into six parts， the book begins with a treatment of matrix algebra， discussing the Schur， Jordan， and singular-value decompositions， the Hadamard and Kronecker products， and more. The second section is the theoretical core of the book and presents a thorough development of the theory of differentials. Practically-oriented， part three contains the rules for working with differentials and lists the differentials of important scalar， vector， and matrix functions. The fourth deals with inequalities， such as Cauchy-Schwarz’s and Minkowski’s， while the fifth section is devoted to applications of matrix differential calculus to the linear regression model. The book closes by detailing maximum likelihood estimation， an ideal source for demonstrating the power of the propagated techniques. Features numerous exercises.

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