MARC details
000 -Record Label |
fixed length control field |
02925nam a22002895i 4500 |
005 - Identificador da versão |
control field |
20181003161029.0 |
010 ## - ISBN - International Standard Book Number |
Número (ISBN) |
978-0-387-98144-4 |
Modalidade de aquisição e/ou preço |
compra |
100 ## - Entrada principal |
Dados gerais de processamento |
20150401d2009 k||y0pory50 ba |
101 ## - Língua do documento |
Língua do texto, banda sonora, etc. |
eng |
102 ## - País da publicação |
País de publicação |
US - United States of America |
200 ## - Título |
Título próprio |
Computational statistics |
Indicação geral da natureza do documento |
Documento eletrónico |
Primeira menção de responsabilidade |
James E. Gentle |
210 ## - Local de edição |
Lugar da edição, distribuição, etc. |
New York |
Nome do editor, distribuidor, etc. |
Springer |
Data da publicação, distribuição, etc. |
2009 |
215 ## - Descrição física (Vol.pg.fl.tm.fsc) |
Descrição física |
XXII, 728 p. |
225 ## - Coleção |
Título próprio da colecção |
Statistics and Computing |
300 ## - Notas gerais |
Texto da nota |
Colocação: Online |
303 ## - Notas Informação descritiva |
Texto da nota |
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. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The first part of the book, consisting of a single long chapter, reviews this background material while introducing computationally-intensive exploratory data analysis and computational inference. The six chapters in the second part of the book are on statistical computing. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. Building on the first chapters on numerical computations and algorithm design, the following chapters cover the main areas of statistical numerical analysis, that is, approximation of functions, numerical quadrature, numerical linear algebra, solution of nonlinear equations, optimization, and random number generation. The third and fourth parts of the book cover methods of computational statistics, including Monte Carlo methods, randomization and cross validation, the bootstrap, probability density estimation, and statistical learning. The book includes a large number of exercises with some solutions provided in an appendix. James E. Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA as well as for other journals in statistics and computing. He is author of Random Number Generation and Monte Carlo Methods and Matrix Algebra. |
606 ## - Nome comum como assunto |
Elemento de entrada |
Estatística matemática |
Subdivisão de assunto |
Processamento de dados |
Koha Internal code |
17449 |
680 ## - Classificação Biblioteca Congresso |
Notação |
QA276 |
700 ## - Autor (resp. principal) |
Palavra de ordem |
Gentle |
Outra parte do nome |
James E. |
Koha Internal Code |
16951 |
801 ## - Fonte de origem |
País |
Portugal |
Regras de catalogação |
RPC |
856 ## - URL Endereço WEB |
URL |
http://dx.doi.org/10.1007/978-0-387-98144-4 |
942 ## - Elementos de entrada adicionados (Koha) |
Fonte da classificação ou esquema de estante |
Library of Congress Classification |
Tipo de item no Koha |
E-Books |
Suprimido |
0 |