🎉 Up to 70% Off Selected ItemsShop Sale
Tensor Methods in Statistics
A pioneering monograph on tensor methods applied to distributional problems arising in statistics, this work constitutes a valuable reference for graduate students and professional statisticians. Prerequisites include some knowledge of linear algebra, eigenvalue decompositions, and linear models as well as likelihood functions and likelihood ratio statistics.
Index notation is the favored mode of expression throughout the book. The first chapter introduces a number of aspects of index notation, groups, invariants, and tensor calculus, with examples drawn from linear algebra, physics, and statistics. Subsequent chapters form the core of the text, addressing moments, cumulants, and invariants. Additional topics include sample cumulants, Edgeworth series, saddlepoint approximation, likelihood functions, and ancillary statistics. More than 200 exercises form an integral part of the text.
Index notation is the favored mode of expression throughout the book. The first chapter introduces a number of aspects of index notation, groups, invariants, and tensor calculus, with examples drawn from linear algebra, physics, and statistics. Subsequent chapters form the core of the text, addressing moments, cumulants, and invariants. Additional topics include sample cumulants, Edgeworth series, saddlepoint approximation, likelihood functions, and ancillary statistics. More than 200 exercises form an integral part of the text.
Reprint of the revised and corrected Chapman and Hall, London and New York, 1987 edition.
mathematics;applied mathematics;stochastic modeling;science;statistics;textbook;tensor methods;linear algebra;eigenvalue decompositions;linear models;likelihood functions;likelihood ratio statistics;index notation;groups;invariants;tensor calculus;sample cumulants;edgeworth series;saddlepoint approximation;ancillary statistics;science and math$19.95
Tensor Methods in Statistics—
$19.95
Product Information
Product Information
Shipping & Returns
Shipping & Returns
Description
A pioneering monograph on tensor methods applied to distributional problems arising in statistics, this work constitutes a valuable reference for graduate students and professional statisticians. Prerequisites include some knowledge of linear algebra, eigenvalue decompositions, and linear models as well as likelihood functions and likelihood ratio statistics.
Index notation is the favored mode of expression throughout the book. The first chapter introduces a number of aspects of index notation, groups, invariants, and tensor calculus, with examples drawn from linear algebra, physics, and statistics. Subsequent chapters form the core of the text, addressing moments, cumulants, and invariants. Additional topics include sample cumulants, Edgeworth series, saddlepoint approximation, likelihood functions, and ancillary statistics. More than 200 exercises form an integral part of the text.
Index notation is the favored mode of expression throughout the book. The first chapter introduces a number of aspects of index notation, groups, invariants, and tensor calculus, with examples drawn from linear algebra, physics, and statistics. Subsequent chapters form the core of the text, addressing moments, cumulants, and invariants. Additional topics include sample cumulants, Edgeworth series, saddlepoint approximation, likelihood functions, and ancillary statistics. More than 200 exercises form an integral part of the text.
Reprint of the revised and corrected Chapman and Hall, London and New York, 1987 edition.
mathematics;applied mathematics;stochastic modeling;science;statistics;textbook;tensor methods;linear algebra;eigenvalue decompositions;linear models;likelihood functions;likelihood ratio statistics;index notation;groups;invariants;tensor calculus;sample cumulants;edgeworth series;saddlepoint approximation;ancillary statistics;science and math
