Numerical Methods of Statistics

Numerical Methods of Statistics

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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 Neldera€“Mead search algorithm.... for Ic1 and Iƒ2 for a given value of Ic2. chex97 Logistic regression (Example 9.7) MLEs for the logistic regression problem of Cox (1970) are computed via the IRWLS algorithm (9.7.4). ... Problem 0 is the linear regression from Exercise 5.5.

Title:Numerical Methods of Statistics
Author: John F. Monahan
Publisher:Cambridge University Press - 2011-04-18

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