01267nam a2200205Ia 4500 CTU_152888 210402s9999 xx 000 0 und d 78.35 512.9434 S375 Schott, James R. Matrix analysis for statistics James R. Schott Hoboken, N.J. Wiley 2005 Matrix Analysis for Statistics, Second Edition provides in-depth, step-by-step coverage of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; the distribution of quadratic forms; and more. The subject matter is presented in a theorem/proof format, allowing for a smooth transition from one topic to another. Proofs are easy to follow, and the author carefully justifies every step. Accessible even for readers with a cursory background in statistics, yet rigorous enough for students in statistics, this new edition is the ideal introduction to matrix analysis theory and practice. Matrices,Mathematical statistics,Ma trận,Thống kê toán học Trọng Hiếu Trung tâm Học liệu Trường Đại học Cần Thơ