13 Ergebnisse.

This textbook for graduate and advanced undergraduate students presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra. Matrix algebra is one of the most important areas of mathematics in data science and in statistical theory, and the second edition of this very popular textbook provides essential updates and comprehensive coverage on...

The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics.  This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became th...

This book describes techniques used in computational statistics and considers some of the areas of applications, such as density estimation and model building, in which computationally intensive methods are useful. In computational statistics, computation is viewed as an instrument of discovery, the role of the computer is not just to store data, perform computations, and produce graphs and tables, but additionally to suggest to the scientist ...

Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers that appear to be random. These "pseudorandom" numbers must pass statistical tests just as random samples would. Methods for producing pseudorandom numbers and transforming those numbers to simulate samples from various distributions are among the most important topics in statistical computing. ...

Accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Regardless of the software system used, the book describes and gives examples of the use of modern computer software for numerical linear algebra. It begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, factorisations, mat...

Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, su...

Computer simulation has become, alongside experimentation and abstract reasoning, a third major tool of science. The student in the computational sciences needs a background in numerical analysis, and it is one of the objectives of this text to provide that background material. This book covers many of the traditional areas of numerical analysis as well as topics in software development and methods of Monte Carlo simulations. It is desi...

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. The first part of this book presents the relevant aspects of the theory of matrix algebra for applications in statistics. This part begins with the fundamental concepts of vectors and vector spaces, next covers the basic algebraic properties of matrices, then describes the analytic properties of vectors and matrices in the multivariat...

This is the fourth in a series of books on statistical computing by James Gentle. TOC: Statistical Methods as Optimization Problems - Numerical Computations - Basic Definitions and Properties of Functions - Solution of Nonlinear Systems of Equations - Unconstrained Descent Methods in Dense Domains - Unconstrained Combinatorial Optimization, Other Direct Search Methods - Optimization Under Constraints - Optimization Under Constraints - Multipl...

Accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Regardless of the software system used, the book describes and gives examples of the use of modern computer software for numerical linear algebra. It begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, factorisations, mat...

Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers that appear to be random. These "pseudorandom" numbers must pass statistical tests just as random samples would. Methods for producing pseudorandom numbers and transforming those numbers to simulate samples from various distributions are among the most important topics in statistical computing. ...

Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, su...

This book describes techniques used in computational statistics and considers some of the areas of applications, such as density estimation and model building, in which computationally intensive methods are useful. In computational statistics, computation is viewed as an instrument of discovery, the role of the computer is not just to store data, perform computations, and produce graphs and tables, but additionally to suggest to the scientist ...