
Symbolic Computation for Statistical Inference
Oxford University Press
Published on 29. June 2000
Book
Hardback
172 pages
978-0-19-850705-5 (ISBN)
Description
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 numerical computation 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 expression numerically 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.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Target group
Professional and scholarly
Illustrations
line figures
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 14 mm
Weight
429 gr
ISBN-13
978-0-19-850705-5 (9780198507055)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Author
Department of StatisticsDepartment of Statistics, University of Toronto, Canada
Department of Public Health SciencesDepartment of Public Health Sciences, University of Toronto, Canada
Content
1. Introduction ; 2. Probability and random variables ; 3. Fundamental procedures ; 4. Asymptotic expansions ; 5. Expansions of expectations, cumulants, and unbiased estimates ; 6. Expansions of distributions ; 7. Expansions for likelihood quantities ; 8. The analytic bootstrap ; 9. Sample surveys ; 10. Intersection matrices