Elements of Statistical Computing
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A01=R.A. Thisted
advanced computational statistics techniques
arithmetic
ASA Board
ASA's Committee
ASA’s Committee
Author_R.A. Thisted
AVL Tree
Biased Cross Validation
Category=PBT
Category=UFM
Category=UY
Conditional Expectation
Data Set
De Hoog
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Floating Point Arithmetic
floating point computation
Floating Point System
Gauss-Seidel method
GCV Estimate
Histogram Estimator
IBM Mainframe
iterative algorithms
Kalman filter applications
Kernel Smoothers
Linear Smoothers
Local Regression Coefficient
missing data techniques
Multiple Linear Regression
Multivariate Outlier Detection Methods
numerical analysis methods
Regression Splines
Ronald A. Thisted
Smoothing Algorithms
Statistical Computing
Usual Regression Computations
Product details
- ISBN 9780412013713
- Weight: 990g
- Dimensions: 156 x 234mm
- Publication Date: 01 Mar 1988
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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.
