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 NelderaMead search algorithm.... for Ic1 and I2 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 |

You must register with us as either a Registered User before you can Download this Book. You'll be greeted by a simple sign-up page.

Once you have finished the sign-up process, you will be redirected to your download Book page.

`1.`Register a free 1 month Trial Account.`2.`Download as many books as you like (Personal use)`3.`Cancel the membership at any time if not satisfied.