Normal view MARC view ISBD view

Numerical methods of statistics

By: Monahan, John F.
Material type: materialTypeLabelBookSeries: Cambridge series in statistical and probabilistic mathematics. Publisher: New York Cambridge University Press 2011Edition: 2nd ed.Description: xvi, 447 p.ISBN: 9781107665934.Subject(s): Mathematical statistics - Data processing | Numerical analysisDDC classification: 519.5 Summary: 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. (http://www.cambridgeindia.org/showbookdetails.asp?ISBN=9781107665934)
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
Item type Current location Item location Call number Status Date due Barcode
Books Vikram Sarabhai Library
Slot 1415 (0 Floor, East Wing) 519.5 M6N8 (Browse shelf) Available 174660

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. (http://www.cambridgeindia.org/showbookdetails.asp?ISBN=9781107665934)

There are no comments for this item.

Log in to your account to post a comment.

Powered by Koha