Numerical Analysis For Statisticians
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Numerical Analysis For Statisticians
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Author : Kenneth Lange
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-05-17
Numerical Analysis For Statisticians written by Kenneth Lange and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-17 with Business & Economics categories.
Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on the principles of numerical analysis and is intended to equip those readers who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods.
Numerical Methods Of Statistics
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Author : John F. Monahan
language : en
Publisher: Cambridge University Press
Release Date : 2001-02-05
Numerical Methods Of Statistics written by John F. Monahan and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-02-05 with Computers categories.
This 2001 book provides a basic background in numerical analysis and its applications in statistics.
Numerical Methods Of Statistics
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Author : John F. Monahan
language : en
Publisher: Cambridge University Press
Release Date : 2011-04-18
Numerical Methods Of Statistics written by John F. Monahan and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-18 with Computers categories.
This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author's website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder–Mead search algorithm.
Numerical Methods Of Statistics
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Author : John Monahan
language : en
Publisher:
Release Date :
Numerical Methods Of Statistics written by John Monahan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Elements Of Statistical Computing
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Author : R.A. Thisted
language : en
Publisher: Routledge
Release Date : 2017-10-19
Elements Of Statistical Computing written by R.A. Thisted and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-19 with Mathematics categories.
Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics. Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques.
Numerical Analysis Statistical Methods
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Author :
language : en
Publisher: Academic Publishers
Release Date :
Numerical Analysis Statistical Methods written by and has been published by Academic Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Computational Statistics
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Author : James E. Gentle
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-28
Computational Statistics written by James E. Gentle and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-28 with Mathematics categories.
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.
Computational Methods For Numerical Analysis With R
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Author : James P Howard, II
language : en
Publisher: CRC Press
Release Date : 2017-07-12
Computational Methods For Numerical Analysis With R written by James P Howard, II and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Mathematics categories.
Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra, interpolation, numerical integration, optimization, and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R, along with examples to demonstrate the function and its use. Computational Methods for Numerical Analysis with R is intended for those who already know R, but are interested in learning more about how the underlying algorithms work. As such, it is suitable for statisticians, economists, and engineers, and others with a computational and numerical background.
Optimization
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Author : Kenneth Lange
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Optimization written by Kenneth Lange and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.
Lange is a Springer author of other successful books. This is the first book that emphasizes the applications of optimization to statistics. The emphasis on statistical applications will be especially appealing to graduate students of statistics and biostatistics.
Computational Statistics
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Author : Yadolah Dodge
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Computational Statistics written by Yadolah Dodge and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Business & Economics categories.
The Role of the Computer in Statistics David Cox Nuffield College, Oxford OXIINF, U.K. A classification of statistical problems via their computational demands hinges on four components (I) the amount and complexity of the data, (il) the specificity of the objectives of the analysis, (iii) the broad aspects of the approach to analysis, (ill) the conceptual, mathematical and numerical analytic complexity of the methods. Computational requi rements may be limiting in (I) and (ill), either through the need for special programming effort, or because of the difficulties of initial data management or because of the load of detailed analysis. The implications of modern computational developments for statistical work can be illustrated in the context of the study of specific probabilistic models, the development of general statistical theory, the design of investigations and the analysis of empirical data. While simulation is usually likely to be the most sensible way of investigating specific complex stochastic models, computerized algebra has an obvious role in the more analyti cal work. It seems likely that statistics and applied probability have made insufficient use of developments in numerical analysis associated more with classical applied mathematics, in particular in the solution of large systems of ordinary and partial differential equations, integral equations and integra-differential equations and for the ¢raction of "useful" in formation from integral transforms. Increasing emphasis on models incorporating specific subject-matter considerations is one route to bridging the gap between statistical ana.