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Numerical Analysis for Statisticians (Statistics and Computing) Corrected Edition

3.3 out of 5 stars 4 customer reviews
ISBN-13: 978-0387949796
ISBN-10: 0387949798
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Editorial Reviews

Review

From a review:

MATHEMATICAL REVIEWS

"This book provides reasonably good coverage of numerical methods that are important in statistical applications. ...but overall the text serves as a good introduction to computational statistics."

 

From the Publisher

Few good people are willing to write books in the area of statistical computing because it changes so fast. The last "new" book of any significance was published in 1988. This book will be very popular both as a reference and as a graduate text for a course on statistical computing.

This book will be an important reference for all statisticians interested in computing and software development.

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Product Details

  • Series: Statistics and Computing
  • Hardcover: 376 pages
  • Publisher: Springer; Corrected edition (April 9, 1999)
  • Language: English
  • ISBN-10: 0387949798
  • ISBN-13: 978-0387949796
  • Product Dimensions: 6.1 x 0.9 x 9.2 inches
  • Shipping Weight: 1.4 pounds (View shipping rates and policies)
  • Average Customer Review: 3.2 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Best Sellers Rank: #3,654,903 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By A Customer on August 29, 2002
Format: Hardcover
The author states in the introduction "My focus on principles of
numerical analysis is intended to equip students to craft their own
software and to understand the advantages and disadvantages of different
numerical methods". Lets look at a few topics to see whether these
lofty goals were achieved.
Least-squares calculations: The chapter on linear regression is nine
pages. The largest section is on the sweep operator (the problems with
the sweep are not mentioned). Solving least squares is thru the normal
equations only (which numerical analysts agree is the least stable of
the "big three" methods for solving least squares problems). There is a
page on woodbury's formula for determinants. Who uses that!? So many
problems in statistics eventually boil down to a least-squares
calculation. This book has almost nothing useful to say about this
problem. How can students "craft their own software" after reading this
book? They simply can't. Look elsewhere.
Eigenvalues: The chapter on eigenvalues is eight pages and covers only
Jacobi's and the Rayleigh quotient, nothing on the QR, nothing on
bidiagonalization. The nine pages would have been better used for
soemthing else.
Bootstrap calculations: I decided to check out section 22.5,
"importance sampling". After a so-so 2-page inroduction we get an
example. Example 22.5.1 uses the "Hormone Patch Data" from Efron and
Tibshirani's Bootstrap book (a wonderful book, by the way). First, the
analysis is botched, the numerator and denominator variables were
interchanged (relative to Efron and Tibshirani).
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Format: Hardcover
Ron Thisted's book on computing algorithms for statisticians was one of the most useful and clearly written texts on the topic. There have also been a few other good ones. Lange brings to the table a more current book that deals with the key new methods such as resampling, Markov chain Monte Carlo, Fourier series and wavelets,the EM algorithm and extensions of it. He also includes useful but uncommon results for power series, exponentiating matrices and continued fraction expansions.
The usual matrix algebra stuff for linear models is also there. You will also find a chapter on nonlinear equations and a chapter on splines. There are asymptotic expansions in Chapter 4 and Edgeworth expansions in Chapter 17. Almost everything that is important in statistical computing today is included.

This book can be used as for a graduate course in statistical computing and is a valuable reference for any statistical researcher.
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Format: Hardcover
Somehow, I had missed the first edition of this book and thus I started reading it this afternoon with a newcomer's eyes (obviously, I will not comment on the differences with the first edition, sketched by the author in the Preface). Past the initial surprise of discovering it was a mathematics book rather than an algorithmic book, I became engrossed into my reading and could not let it go! Numerical Analysis for Statisticians, by Kenneth Lange, is a wonderful book. It provides most of the necessary background in calculus and some algebra to conduct rigorous numerical analyses of statistical problems. This includes expansions, eigen-analysis, optimisation, integration, approximation theory, and simulation, in less than 600 pages. It may be due to the fact that I was reading the book in my garden, with the background noise of the wind in tree leaves, but I cannot find any solid fact to grumble about! Not even about the MCMC chapters! I simply enjoyed Numerical Analysis for Statisticians from beginning till end.

From the above, it may sound as if Numerical Analysis for Statisticians does not fulfill its purpose and is too much of a mathematical book. Be assured this is not the case: the contents are firmly grounded in calculus (analysis) but the (numerical) algorithms are only one code away. An illustration (among many) is found in Section 8.4: Finding a Single Eigenvalue, where Kenneth Lange shows how the Raleigh quotient algorithm of the previous section can be exploited to this aim, when supplemented with a good initial guess based on Gerschgorin's circle theorem. This is brilliantly executed in two pages and the code is just one keyboard away. The EM algorithm is immersed into a larger MM perspective.
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Format: Kindle Edition Verified Purchase
I bought this book for Kindle, believing that I would get the (recent) second edition - Amazon's Kindle page for this item has as illustration the cover of the second edition! However the ten year old "first edition" was delivered to my Kindle. I later also found out that one could by an eBook second edition directly from Springer publishers for less than half the price.

Fortunately it turned out that it is not so difficult as I thought to "return" a mistaken Kindle purchase, via the "Manage my Kindle" page.

In conclusion: fantastic book, but make sure you get the second edition, and if you want an eBook version, don't buy it on Amazon for the time being!
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