Computational Statistics
Resource Information
The work Computational Statistics represents a distinct intellectual or artistic creation found in University of Manitoba Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Computational Statistics
Resource Information
The work Computational Statistics represents a distinct intellectual or artistic creation found in University of Manitoba Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Computational Statistics
 Statement of responsibility
 by James E. Gentle
 Subject

 Numeric Computing
 Data Mining and Knowledge Discovery
 Mathematical statistics
 Data mining
 Computer science
 Distribution (Probability theory
 Statistics and Computing/Statistics Programs
 Computational Mathematics and Numerical Analysis
 Mathematics of Computing
 Probability Theory and Stochastic Processes
 Electronic data processing
 Computer science  Mathematics
 Language

 eng
 eng
 Summary
 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 computationallyintensive 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 computationallyintensive 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
 Dewey number
 519.50285
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
 l8WZTP3NUhE
 Language note
 English
 LC call number

 QA273.A1274.9
 QA274274.9
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Statistics and Computing,
Context
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