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 | | Symbolic Computation for Statistical Inference (Oxford Statistical Science Series) by D. F. Andrews, J. E. Stafford ISBN-10: 9780198507055 ISBN-10: 0-19-850705-4 ISBN-13: 9780198507055 ISBN-13: 978-0-19-850705-5 Hardcover 2000-08-24 Oxford University Press, USA Find Lowest Price |
Editorials | ||
Product 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: 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. | ||
Reviews | ||
good text on use of mathematical languages in stats When I was a graduate student at Stanford I labored with tedious algebraic computations while trying to prove limit theorems for maxima of stationary sequences. Often it would be very frustrating as I would spend hours checking calculations only to repeatedly make minor mistakes that would cause results not to match. When properly programmed, this is ideal work for a computer. The computer can do the algebra flawlessly and much more quickly freeing the researcher to think about the harder problems. Mathematica was just coming out in the late 1970s but it was too late for my use. Now symbolic computing has advanced to the stage where we all should be using it. Andrews and Stafford make that point in this book. They cover many of the important mathematical results that are needed to solved advanced statistical problems. Edgeworth expansions, moment calculations and Taylor series expansions can all involve a great deal of tedious algebra. All these methods and their applications are exposed in this marvelous book. The authors also introduce us to an analytic bootstrap, Bartlett corrections, saddlepoint approximations, Cornish-Fisher expansions and Edgeworth expansions and applications to survey sampling designs. Chapter 3 goes into the fundamentals of symbolic computing and is the foundation needed for the applications that follow. This is a unique book that is valuable to any research statistician who may need to develop asymptotic results. | ||
a variety of statistical applications of symbol computation When I was a graduate student at Stanford I labored with tedious algebraic computations while trying to prove limit theorems for maxima of stationary sequences. Often it would be very frustrating as I would spend hours checking calculations only to repeatedly make minor mistakes that would cause results not to match. When properly programmed, this is ideal work for a computer. The computer can do the algebra flawlessly and much more quickly freeing the researcher to think about the harder problems. Mathematica was just coming out in the late 1970s but it was too late for my use. Now symbolic computing has advanced to the stage where we all should be using it. Andrews and Stafford make that point in this book. They cover many of the important mathematical results that are needed to solved advanced statistical problems. Edgeworth expansions, moment calculations and Taylor series expansions can all involve a great deal of tedious algebra. All these methods and their applications are exposed in this marvelous book. The authors also introduce us to an analytic bootstrap, Bartlett corrections, saddlepoint approximations, Cornish-Fisher expansions and Edgeworth expansions and applications to survey sampling designs. Chapter 3 goes into the fundamentals of symbolic computing and is the foundation needed for the applications that follow. This is a unique book that is valuable to any research statistician who may need to develop asymptotic results. | ||