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Matrix Tricks for Linear Statistical Models

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Cover of 'Matrix Tricks for Linear Statistical Models'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Easy Column Space Tricks
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    Chapter 3 Easy Projector Tricks
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    Chapter 4 Easy Correlation Tricks
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    Chapter 5 Generalized Inverses in a Nutshell
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    Chapter 6 Rank of the Partitioned Matrix and the Matrix Product
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    Chapter 7 Rank Cancellation Rule
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    Chapter 8 Sum of Orthogonal Projectors
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    Chapter 9 A Decomposition of the Orthogonal Projector
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    Chapter 10 Minimizing cov(y - Fx)
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    Chapter 11 BLUE
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    Chapter 12 General Solution to AYB = C
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    Chapter 13 Invariance with Respect to the Choice of Generalized Inverse
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    Chapter 14 Block-Diagonalization and the Schur Complement
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    Chapter 15 Nonnegative Definiteness of a Partitioned Matrix
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    Chapter 16 The Matrix $$\dot{\rm M}$$
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    Chapter 17 Disjointness of Column Spaces
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    Chapter 18 Full Rank Decomposition
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    Chapter 19 Eigenvalue Decomposition
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    Chapter 20 Singular Value Decomposition
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    Chapter 21 The Cauchy–Schwarz Inequality
Attention for Chapter 8: Sum of Orthogonal Projectors
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