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Elements of Statistical Computing: NUMERICAL COMPUTATION, by R. A. Thisted
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Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics. Included are discussions of numerical analysis, numerical integration, and smoothing.
The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques.
- Sales Rank: #2163087 in Books
- Published on: 1988-03-01
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x 1.00" w x 6.14" l, 1.77 pounds
- Binding: Hardcover
- 448 pages
Review
Very readable...This is an authoritative and well-written book which is also of considerable practical use...It is highly recommended.
eadable...This is an authoritative and well-written book which is also of considerable practical use...It is highly recommended.
Most helpful customer reviews
5 of 5 people found the following review helpful.
Thisted's book is very good
By A Customer
This book provides a lucid and very informative account of Statistical Computation as of the late 80's. It covers many topics not covered in other books on the subject such as Kennedy and Gentle (1980): Statistical Computing, Marcel Dekker. It is very good when it deals with matrix computations and their uses in Statistics. However, it does not adequately deal with simulation and the consequent statistical techniques, which have gained importance since the book was published due to their seminal use in Markov Chain Monte Carlo methods. Nonetheless, it is an excellent book and one of my favourites.
0 of 0 people found the following review helpful.
A great source on computing.
By ssray
I have more than 100 books on Statistics and quite a few on statistical computing, but this is among the five or six books that I have used the most in the 12 years of my career as a Statistician. The other day, I opened Givens and Hoeting (2nd Edition, 2013) with the hope to learn something on adaptive quadrature methods of integration. That book has only a few lines on the topic and refers to three books and Thisted's book is one of the three. This single incident suggests how good Thisted's book is. This book rarely disappoints.
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