Last edited by Meztikasa
Tuesday, February 11, 2020 | History

6 edition of Computational statistics found in the catalog.

Computational statistics

  • 110 Want to read
  • 16 Currently reading

Published by Wiley-Interscience in Hoboken, N.J, [Chichester] .
Written in English

    Subjects:
  • Mathematical statistics -- Data processing.

  • Edition Notes

    Includes bibliographical references (p. 379-406) and index.

    StatementGeof H. Givens, Jennifer A. Hoeting.
    SeriesWiley series in probability and statistics
    ContributionsHoeting, Jennnifer A. 1966-
    Classifications
    LC ClassificationsQA276.4 .G58 2005
    The Physical Object
    Paginationxix, 418 p. :
    Number of Pages418
    ID Numbers
    Open LibraryOL3438378M
    ISBN 100471461245
    LC Control Number2005297238
    OCLC/WorldCa56538617

    The book also contains many exercises that substantiate the concepts, with solutions and hints in the appendix, an extensive bibliography, and a link to further literature and notes. James E. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. James E.

    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. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. 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 also would be very useful to engineers and scientists who are well trained in statistics.

    About the authors James E. There are exercises provided for each chapter together with some solutions. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model. Navy as an operations research analyst and a computer scientist. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. Her research interests include text data mining, probability density estimation, signal processing, scientific visualization, and statistical pattern recognition.


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Computational statistics book

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.

At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. I strongly recommend this book to anyone who analyzes data using either a commercial statistical software package or statistical computer programs written by the user or someone else.

The six chapters in the second part of the book are on statistical computing. Martinez is fully retired after a long career with the U. A comprehensive statistics Computational statistics book for both beginners and advanced analysts.

A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages.

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. Bureau of Labor Statistics.

The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.

The book includes a large number of exercises with some solutions provided in an appendix. It presents analytic approaches and statistical methods Computational statistics book in science, business, industry, and data mining, written for the real-life practitioner of these 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. Reviews Praise for Previous Editions: "… useful as a reference where one can look to get a concise description of a statistical methodology and MATLAB code that can be used to implement it … the book is excellent.

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.

Review Text From the reviews: "This is a book that covers many of the computational issues that statisticians will encounter as part of their research and applied work. Seltman - Carnegie Mellon University This book is intended as required reading material for the course Experimental Design for the Behavioral and Social Sciences, a second level statistics course for undergraduate students in the College of Humanities and Social Sciences This is an excellent first edition of a text that I hope to use the next time I teach a statistical computing course.

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 target readership includes undergraduates, postgraduates in statistics and allied fields such as computer science and mathematics, scientific research workers, and practitioners of statistics and numerical techniques. 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. Thus this book is important not only for data oriented statisticians but for econometricians, psychometricians, political methodologists and biometricians as well.Purchase Computational Statistics with R, Volume 32 - 1st Edition.

Print Book & E-Book. ISBN 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.

Lately as I have gotten deeper in Mathematical Statistics, I notice I have more difficulties thinking about how to program the simulation itself. That said, I would like to know if any of you have some books about Simulations and Computational Statistics you particularly recommend.

The definition of what is meant by statistics and statistical analysis has changed considerably over the last few decades. Here are two contrasting definitions of what statistics is, from eminent professors in the field, some 60+ years apart: "Statistics is the branch of scientific method which deals with the data obtained by counting or.

Computational Statistics, 2nd Edition

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. If you have never studied statistics, I think this book is a good place to start. And if you have taken a traditional statistics class, I hope this book will help repair the damage. | Allen B. Downey is a Professor of Computer Science at the Franklin W.

Computational Statistics Handbook with MATLAB

Olin College of Engineering in Needham, MA. Contributor List.