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Numerical Analysis for Statisticians (Paperback, Softcover reprint of hardcover 2nd ed. 2010)
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Numerical Analysis for Statisticians (Paperback, Softcover reprint of hardcover 2nd ed. 2010)
Series: Statistics and Computing
Expected to ship within 10 - 15 working days
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Every advance in computer architecture and software tempts
statisticians to tackle numerically harder problems. To do so
intelligently requires a good working knowledge of numerical
analysis. This book equips students to craft their own software and
to understand the advantages and disadvantages of different
numerical methods. Issues of numerical stability, accurate
approximation, computational complexity, and mathematical modeling
share the limelight in a broad yet rigorous overview of those parts
of numerical analysis most relevant to statisticians. In this
second edition, the material on optimization has been completely
rewritten. There is now an entire chapter on the MM algorithm in
addition to more comprehensive treatments of constrained
optimization, penalty and barrier methods, and model selection via
the lasso. There is also new material on the Cholesky
decomposition, Gram-Schmidt orthogonalization, the QR
decomposition, the singular value decomposition, and reproducing
kernel Hilbert spaces. The discussions of the bootstrap,
permutation testing, independent Monte Carlo, and hidden Markov
chains are updated, and a new chapter on advanced MCMC topics
introduces students to Markov random fields, reversible jump MCMC,
and convergence analysis in Gibbs sampling. Numerical Analysis for
Statisticians can serve as a graduate text for a course surveying
computational statistics. With a careful selection of topics and
appropriate supplementation, it can be used at the undergraduate
level. It contains enough material for a graduate course on
optimization theory. Because many chapters are nearly
self-contained, professional statisticians will also find the book
useful as a reference.
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