This 2001 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 provides a basic background in numerical analysis emphasizing 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 application of numerical tools: numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. The book concludes with an examination of sorting, FFT and the application of other 'fast' algorithms to statistics. Each chapter contains exercises that range from the simple to research problems, as well as examples of the methods at work.
Stochastic integration rules are derived for infinite integration intervals, generalizing rules developed by Siegel and O'Brien [SIAM J. Sci. Statist. Comput., 6 (1985), pp. 169-181] for finite intervals. Then random orthogonal transformations of rules for integrals over the surface of the unit m-sphere are used to produce stochastic rules for these integrals. The two types of rules are combined to produce stochastic rules for multidimensional integrals over infinite regions with Normal or Student-t weights. Example results are presented to illustrate the effectiveness of the new rules.
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