Computational Statistics (Wiley Series in Computational Statistics)

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Product Description

This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. � The book is comprised of four main parts spanning the field:

• Optimization
• Integration and Simulation
• Bootstrapping
• Density Estimation and Smoothing

Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods.  The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data.� The book website now includes comprehensive R code for the entire book.� There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.

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Computational Statistics (Wiley Series in Computational Statistics) Review

I am using this as the main textbook in one of my courses on Computational Statistics. After going through the book for four months now, I feel the book does a reasonably good job of explaining the theoretical under pinning of the subject area without getting too mathematical (given the subject, its impossible to do it without mathematics). However, I think many examples that the book takes up could have been discussed in more detail. Very often, while going through the examples, I felt that the authors gloss over several details which might not be that obvious to a reader.
Also, for the end of the chapter programming exercises, it might be worthwhile to give some point estimates of the final solution if possible. Otherwise, attempting them just becomes equivalent to shooting in the dark.

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