Symbolic Computation for Statistical Inference

Hardcover | June 15, 2000

byD. F. Andrews, J. E. Stafford

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Over recent years, developments in statistical computing have freed statisticians from the burden of calculation and have made possible new methods of analysis that previously would have been too difficult or time-consuming. Up till now these developments have been primarily in numericalcomputation and graphical display, but equal steps forward are now being made in the area of symbolic computing, or in other words the use of computer languages and procedures to manipulate expressions. This allows researchers to compute an algebraic expression, rather than evaluate the expressionnumerically over a given range. This book summarizes a decade of research into the use of symbolic computation applied to statistical inference problems. It shows the considerable potential of the subject to automate statistical calculation, leaving researchers free to concentrate on new concepts.Starting with the development of algorithms applied to standard undergraduate problems, the book then goes on to develop increasingly more powerful tools. Later chapters then discuss the application of these algorithms to different areas of statistical methodology.

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Over recent years, developments in statistical computing have freed statisticians from the burden of calculation and have made possible new methods of analysis that previously would have been too difficult or time-consuming. Up till now these developments have been primarily in numericalcomputation and graphical display, but equal step...

D. F. Andrews is at University of Toronto. J. E. Stafford is at University of Toronto.

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Format:HardcoverPublished:June 15, 2000Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198507054

ISBN - 13:9780198507055

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Table of Contents

1. Introduction2. Probability and random variables3. Fundamental procedures4. Asymptotic expansions5. Expansions of expectations, cumulants, and unbiased estimates6. Expansions of distributions7. Expansions for likelihood quantities8. The analytic bootstrap9. Sample surveys10. Intersection matrices