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, such as the PDF decomposition, that arise in a wide range of methods. Provides a comprehensive coverage of modern computationally-intensive statistical methods Covers the topics in numerical analysis necessary for accurate and efficient implementation of the methods of computational statistics Emphasizes the unity of the methods of computational inference INDICE: Mathematical and statistical preliminaries.- Computer storage and arithmetic.- Algorithms and programming.- Approximation of functions and numerical quadrature.- Numerical linear algebra.- Solution of nonlinear equations and optimization.- Generation of random numbers.- Graphical methods in computational statistics.- Tools for identification of structure in data.- Estimation of functions.- Monte Carlo methods for statistical inference.- Data randomization, partitioning, and augmentation.- Bootstrap methods.- Estimation of probability density functions using parametric models.- Nonparametric estimation of probability density functions.- Statistical learning and data mining.- Statistical models of dependencies.
- ISBN: 978-0-387-98143-7
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 714
- Fecha Publicación: 01/08/2009
- Nº Volúmenes: 1
- Idioma: Inglés