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Basics of Matrix Algebra for Statistics with R
출판사 : CRC
저 자 : Fieller
ISBN : 9781498712361
발행일 : 2015-7
도서종류 : 외국도서
발행언어 : 영어
페이지수 : 248
판매가격 : 32,000원
판매여부 : 재고확인요망
6.5 x 0.5 x 9.5 inches :
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   Basics of Matrix Algebra for Statistics with R 목차
Table of Contents

Further Reading
Guide to Notation
An Outline Guide to R
Inputting Data to R
Summary of Matrix Operators in R
Examples of R Commands
Vectors and Matrices
Matrix Arithmetic
Transpose and Trace of Sums and Products
Special Matrices
Partitioned Matrices
Algebraic Manipulation of matrices
Useful Tricks
Linear and Quadratic Forms
Creating Matrices in R
Matrix Arithmetic in R
Initial Statistical Applications
Rank of Matrices
Introduction and Definitions
Rank Factorization
Rank Inequalities
Rank in Statistics
Introduction and Definitions
Implementation in R
Properties of Determinants
Orthogonal Matrices
Determinants of Partitioned Matrices
A Key Property of Determinants
Introduction and Definitions
Implementation in R
Inverses of Patterned Matrices
Inverses of Partitioned Matrices
General Formulae
Initial Applications Continued
Eigenanalysis of Real Symmetric Matrices
Introduction and Definitions
Implementation in R
Properties of Eigenanalyses
A Key Statistical Application: PCA
Matrix Exponential
Eigenanalysis of Matrices with Special Structures
Summary of Key Results
Vector and Matrix Calculus
Differentiation of a Scalar with Respect to a Vector
Differentiation of a Scalar with Respect to a Matrix
Differentiation of a Vector with Respect to a Vector
Differentiation of a Matrix with Respect to a Scalar
Use of Eigenanalysis in Constrained Optimization
Further Topics
Further Matrix Decompositions
Generalized Inverses
Hadamard Products
Kronecker Products and the Vec Operator
Key Applications to Statistics
The Multivariate Normal Distribution
Principal Component Analysis
Linear Discriminant Analysis
Canonical Correlation Analysis
Classical Scaling
Linear Models
Outline Solutions to Exercises


   도서 상세설명   


Covers basic algebraic manipulation of matrices, such as basic arithmetic, inversion, partitioning, rank, determinants, decompositions, eigenanalysis, and Hadamard and Kronecker products
Shows how to implement the techniques in R using worked numerical examples
Describes vector and matrix calculus, including differentiation of scalars and linear and quadratic forms
Incorporates useful tricks, such as identifying rank 1 matrices and scalar subfactors within products
Explains how to convert an optimization problem to an eigenanalysis by imposing a non-restrictive constraint
Presents the derivation of key results in linear models and multivariate methods with step-by-step cross-referenced explanations
Includes numerous theoretical and numerical exercises for self-assessment

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