       PACKT (406) Text Book 교재용원서 (673) 컴퓨터공학 (821) 컴퓨터 일반도서 (556) 전기,전자공학 (713) 기계공학 (199) 재료공학 (34) 에너지공학 (65) 의용공학 (39) 생명과학 (229) 물리학 (427) 지구과학 (74) 천문학 (39) 수학 (103) 통계학 (46) 경영학 (42) 산업공학 (12) 사회복지학 (5) 심리학 (247) 교육학 (1) 화학 (5) 기타 (64) 특가할인도서 (택배비별도) (87)     홈 > > 수학   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 : 주문수량 : 개    Basics of Matrix Algebra for Statistics with R 목차
 Table of Contents Introduction Objectives 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 Vectors 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 Determinants Introduction and Definitions Implementation in R Properties of Determinants Orthogonal Matrices Determinants of Partitioned Matrices A Key Property of Determinants Inverses Introduction and Definitions Properties Implementation in R Inverses of Patterned Matrices Inverses of Partitioned Matrices General Formulae Initial Applications Continued Eigenanalysis of Real Symmetric Matrices Introduction and Definitions Eigenvectors Implementation in R Properties of Eigenanalyses A Key Statistical Application: PCA Matrix Exponential Decompositions Eigenanalysis of Matrices with Special Structures Summary of Key Results Vector and Matrix Calculus Introduction 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 Introduction Further Matrix Decompositions Generalized Inverses Hadamard Products Kronecker Products and the Vec Operator Key Applications to Statistics Introduction The Multivariate Normal Distribution Principal Component Analysis Linear Discriminant Analysis Canonical Correlation Analysis Classical Scaling Linear Models Outline Solutions to Exercises Bibliography Index 도서 상세설명 Features 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 교육용 보조자료 작성된 교육용 보조자료가 없습니다.     