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Elements of Statistical Computing

NUMERICAL COMPUTATION

By: R.A. Thisted

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Elements of Statistical Computing

NUMERICAL COMPUTATION

By: R.A. Thisted

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.

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Page Count

456

Publisher

Routledge

Published Date

ISBN 10

1351452746

ISBN 13

9781351452748

Elements of Statistical Computing

NUMERICAL COMPUTATION

By: R.A. Thisted

Write a review Read reviews Take a Quiz Solve Book Puzzle

Elements of Statistical Computing

NUMERICAL COMPUTATION

By: R.A. Thisted

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.

Average ratings

N/A

5

0%

4

0%

3

0%

2

0%

1

0%

Solve jigsaw puzzle

Age

Page Count

456

Publisher

Routledge

Published Date

ISBN 10

1351452746

ISBN 13

9781351452748