Mathematical Statistics with Mathematica (Springer Texts in Statistics) - Hardcover

Über die Autorin bzw. den Autor

Colin Rose ist Gründer und Vorsitzender der Accelerated Learning Systems Ltd. in England. Er ist Spezialist für praktische Lernfragen und berät neben großen Unternehmen auch die englische Regierung

Von der hinteren Coverseite

This path-breaking book presents a unified approach for doing mathematical statistics with Mathematica. The included mathStatica software builds upon Mathematica's symbolic engine to create a sophisticated toolset specially designed for doing mathematical statistics. With mathStatica, students can easily solve difficult statistical problems, while the professional statistician will be able to tackle tricky multivariate

distributions, generating functions, inversion theorems, symbolic ML estimation, unbiased estimation, etc. The mathStatica software is wonderfully easy to use, and yet so powerful that it can find corrections to mainstream reference texts and solve new problems in seconds. This book is the ideal companion for researchers and students in statistics, econometrics, engineering, physics, psychometrics, economics, finance,

biometrics and the social sciences, across both the pure and applied domains.

The book contains two cross-platform CDs, which run on Windows, Mac, Linux, and most flavours of UNIX:

CD 1 - mathStatica CD-ROM containing:

* mathStatica: the Application Pack for mathematical statistics

* live interactive book that is identical to the printed text

* hundreds of live examples, animations and illustrations

* custom Mathematica palettes

CD 2 - Mathematica v4 (trial CD): for readers who are new to Mathematica.

mathStatica replaces dozens of reference works, extending analysis

to problems of arbitrary high order. Features include:

* a complete suite of functions for manipulating probability density

functions

* automated expectations, probability, plotting

* automated transformations (functions of random variables)

* symbolic maximum likelihood estimation

* numerical maximum likelihood estimation

* automated Pearson curve fitting

* Johnson curve fitting

* Gram--Charlier expansions

* non-parametric kernel density estimation

* moment conversion formulae

* component-mix and parameter-mix distributions

* stable distributions

* copulae

* random number generation

* asymptotics

* decision theory

* order statistics

* Fisher Information

* h-statistics, k-statistics, polykays

Colin Rose is director of the Theoretical Research Institute (Sydney). He holds a PhD from the University of Sydney. In 1998/9, he was a Visiting Scholar at Wolfram Research, the makers of Mathematica. He has published in leading international journals on computer algebra systems and their application to statistics, economics and finance. His work has been presented at venues such as Oxford, London School of Economics, the Bank of England, and the NBER.

Murray D. Smith is a senior lecturer in the Discipline of Econometrics and Business Statistics at the University of Sydney. He holds a first-class Honours degree and a PhD from Monash University. In 1998-1999, he was awarded an Alexander von Humboldt Research Fellowship at the University of Munich. He publishes in the fields of statistics,

econometrics, and computer algebra systems.